Machine learning is reaching notable success when solving complex tasks in many fields. This course serves as in introduction to basic machine learning concepts and techniques, focusing both on the theoretical foundation, and on implementation and utilization of machine learning algorithms in Python programming language. High attention is paid to the ability of application of the machine learning techniques on practical tasks, in which the students try to devise a solution with highest performance.
Python programming skills are required, together with basic probability theory knowledge.
Official name: Introduction to Machine Learning with Python
SIS code: NPFL129
Semester: winter
E-credits: 5
Examination: 2/2 C+Ex
Instructors: Jindřich Libovický (lecture),
Zdeněk Kasner,
Tomáš Musil (practicals),
Milan Straka (assignments & ReCodEx),
Petr Kašpárek, Marek Seltenhofer, Matej Straka (teaching assistants)
This course is also part of the inter-university programme prg.ai Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at prg.ai/minor.
All lectures and practicals will be recorded and available on this website.
After this course students should…
1. Introduction to Machine Learning Slides PDF Slides CS Lecture CS Practicals EN Practicals EN Practicals Slides linear_regression_manual linear_regression_features Questions
2. Linear Regression, SGD Slides PDF Slides CS Lecture CS Practicals EN Practicals linear_regression_l2 linear_regression_sgd feature_engineering rental_competition Questions
3. Peceptron, Logistic Regression Slides PDF Slides CS Lecture CS Practicals EN Lecture EN Practicals perceptron logistic_regression_sgd grid_search thyroid_competition Questions
4. Multiclass Logistic Regression, Multilayer Perceptron Slides PDF Slides CS Lecture CS Practicals EN Lecture EN Practicals (part) softmax_classification_sgd mlp_classification_sgd mnist_competition Questions
5. MLP, Softmax as MaxEnt classifier, F1 score Slides PDF Slides multilabel_classification_sgd diacritization CS Lecture CS Practicals EN Lecture EN Practicals Questions
6. Representing Text (TF-IDF, Word2Vec) Slides PDF Slides tf_idf imdb_sentiment diacritization_dictionary CS Lecture CS Practicals EN Lecture EN Practicals Questions
7. K Nearest Neighbors, Naive Bayes Slides PDF Slides k_nearest_neighbors naive_bayes isnt_it_ironic CS Lecture EN Lecture EN Practicals Questions
8. Correlation, Model Combination Slides PDF Slides metric_correlation miniaturization CS Lecture CS Practicals EN Lecture EN Practicals Questions
9. Decision Trees, Random Forests Slides PDF Slides decision_tree random_forest human_activity_recognition CS Lecture CS Practicals EN Lecture 1/2 EN Lecture 2/2 EN Practicals Questions
10. Gradient Boosted Decision Trees Slides PDF Slides gradient_boosting nli_competition CS Lecture CS Practicals EN Lecture EN Practicals Questions
11. SVD, PCA, k-means Slides PDF Slides CS Lecture CS Practicals EN Lecture EN Practicals pca kmeans Questions
12. Statistical Hypothesis Testing, Model Comparison Slides PDF Slides CS Lecture CS Practicals EN Lecture EN Practicals bootstrap_resampling permutation_test Questions
13. Machine Learning Ethics, Final Summary Slides PDF Slides CS Lecture EN Lecture EN Practicals Questions
Unless otherwise stated, teaching materials for this course are available under CC BY-SA 4.0.
The lecture content, including references to some additional study materials. The main study material is the Pattern Recognition and Machine Learning by Christopher Bishop, referred to as PRML.
Note that the topics in italics are not required for the exam.
Oct 03 Slides PDF Slides CS Lecture CS Practicals EN Practicals EN Practicals Slides linear_regression_manual linear_regression_features Questions
Learning objectives. After the lecture you shoud be able to…
Covered topics and where to find more:
After the lecture: short and non-comprehensive recap quiz.
Oct 10 Slides PDF Slides CS Lecture CS Practicals EN Practicals linear_regression_l2 linear_regression_sgd feature_engineering rental_competition Questions
Learning objectives. After the lecture you shoud be able to
Covered topics and where to find more:
After the lecture: short and non-comprehensive recap quiz.
Oct 17 Slides PDF Slides CS Lecture CS Practicals EN Lecture EN Practicals perceptron logistic_regression_sgd grid_search thyroid_competition Questions
Learning objectives. After the lecture you shoud be able to
Covered topics and where to find more:
After the lecture: short and non-comprehensive recap quiz.
Oct 24 Slides PDF Slides CS Lecture CS Practicals EN Lecture EN Practicals (part) softmax_classification_sgd mlp_classification_sgd mnist_competition Questions
Learning objectives. After the lecture you shoud be able to
Covered topics and where to find more:
After the lecture: short and non-comprehensive recap quiz.
Oct 31 Slides PDF Slides multilabel_classification_sgd diacritization CS Lecture CS Practicals EN Lecture EN Practicals Questions
Learning objectives. After the lecture you shoud be able to
Covered topics and where to find more:
Nov 7 Slides PDF Slides tf_idf imdb_sentiment diacritization_dictionary CS Lecture CS Practicals EN Lecture EN Practicals Questions
Learning objectives. After the lecture you shoud be able to
Covered topics and where to find more:
After the lecture: short and non-comprehensive recap quiz.
Nov 14 Slides PDF Slides k_nearest_neighbors naive_bayes isnt_it_ironic CS Lecture EN Lecture EN Practicals Questions
Learning objectives. After the lecture you shoud be able to
Covered topics and where to find more:
After the lecture: short and non-comprehensive recap quiz.
Nov 21 Slides PDF Slides metric_correlation miniaturization CS Lecture CS Practicals EN Lecture EN Practicals Questions
Learning objectives. After the lecture you shoud be able to
Explain and implement different ways of measuring correlation: Pearson's correlation, Spearman's correlation, Kendall's $\tau$.
Decide if correlation is a good metric for your model.
Measure inter-annotator agreement and draw conclusions for data cleaning and for limits of your models.
Use correlation with human judgment to validate evaluation metrics.
Ensemble models with uncorrelated predictions.
Distill ensembles into smaller models.
Covered topics and where to find more:
After the lecture: short and non-comprehensive recap quiz.
Nov 28 Slides PDF Slides decision_tree random_forest human_activity_recognition CS Lecture CS Practicals EN Lecture 1/2 EN Lecture 2/2 EN Practicals Questions
Learning objectives. After the lecture you shoud be able to
Implement Decision Trees and Random Forests for classification and regression
Explain how the splitting criterion depend on optimized loss function
Tell how Random Forests differ from Gradient Boosted Decision Trees
Covered topics and where to find more:
Decision trees [Section 14.4 of PRML]
Random forests
After the lecture: short and non-comprehensive recap quiz.
Dec 05 Slides PDF Slides gradient_boosting nli_competition CS Lecture CS Practicals EN Lecture EN Practicals Questions
Learning objectives. After the lecture you shoud be able to
Explain second-order optimization methods
Implement gradient boosted decision trees for regression and classification
Decide what supervised machine learning approach is suitable for particular problems
Covered topics and where to find more:
Gradient boosting decision trees [Paper XGBoost: A Scalable Tree Boosting System]
After the lecture: short and non-comprehensive recap quiz.
Dec 12 Slides PDF Slides CS Lecture CS Practicals EN Lecture EN Practicals pca kmeans Questions
Learning objectives. After the lecture you shoud be able to
Theoretically explain Singular Value Decomposition (SVD), prove it exists and explain what the Eckart-Young theorem says.
Theoretically explain Principal Component Analysis (PCA) and say how it explains the variance in the data based on SVD.
Use SVD or PCA for dimensionality reduction, data visualization and data whitening.
Implement the $k$-means algorithm and use it for clustering.
Covered topics and where to find more:
Singular value decomposition [Gilbert Strang: Linear Algebra and Learning from Data. Wellesley- Cambridge Press, 2019. Chapters I.8, I.9.]
Principal component analysis [Sections 12.1 and 12.4.2 of PRML]
Power iteration algorithm
K-Means clustering [Section 9.1 of PRML]
After the lecture: short and non-comprehensive recap quiz.
Dec 19 Slides PDF Slides CS Lecture CS Practicals EN Lecture EN Practicals bootstrap_resampling permutation_test Questions
Learning objectives. After the lecture you shoud be able to
Explain foundations of statistical hypothesis testing.
Reason about multiple comparison problem.
Use Bootstrap Resampling and Permutation Tests to compare machine learning models.
Covered topics and where to find more:
Jan 09 Slides PDF Slides CS Lecture EN Lecture EN Practicals Questions
Learning objectives. After the lecture you shoud be able to
Explain what main theoretical ethical frameworks are.
Reason about ethical problems in various stages of developing ML System.
Covered topics and where to find more:
Main ethical frameworks: deontology, utilitarism
Ethical problem examples
Problem definition (some takes are inherently problematic)
Data collection (biases, unethical acquisition)
Modeling problems (bias amplification via discretization, privacy violation via memorization)
Evaluation problems (misleading metrics, unexpected consequences of optimization)
Deployment (train-test mismatch, feedback loops)
More materials:
Rachel Thomas: 11 Short Machine Learning Ethics Videos
Mark Coeckelbergh: AI Ethics / Etika umělé inteligence
To pass the practicals, you need to obtain at least 70 points, excluding the bonus points. Note that up to 40 points above 70 (both bonus and non-bonus) will be transfered to the exam. In total, assignments for at least 105 points (not including the bonus points) will be available.
The tasks are evaluated automatically using the ReCodEx Code Examiner.
The evaluation is performed using Python 3.11, scikit-learn 1.3.0, numpy 1.24.2, scipy 1.11.2, pandas 2.1.0, and matplotlib 3.7.2. You should install the exact version of these packages yourselves.
Solving assignments in teams (of size at most 3) is encouraged, but everyone has to participate (it is forbidden not to work on an assignment and then submit a solution created by other team members). All members of the team must submit in ReCodEx individually, but can have exactly the same sources/models/results. Each such solution must explicitly list all members of the team to allow plagiarism detection using this template.
Cheating is strictly prohibited and any student found cheating will be punished. The punishment can involve failing the whole course, or, in grave cases, being expelled from the faculty. While discussing assignments with any classmate is fine, each team must complete the assignments themselves, without using code they did not write (unless explicitly allowed). Of course, inside a team you are allowed to share code and submit identical solutions.
Deadline: Oct 17, 7:59 a.m. 3 points
Starting with the linear_regression_manual.py template, solve a linear regression problem using the algorithm from the lecture which explicitly computes the matrix inversion. Then compute root mean square error on the test set.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 linear_regression_manual.py --test_size=0.1
52.38
python3 linear_regression_manual.py --test_size=0.5
54.58
python3 linear_regression_manual.py --test_size=0.9
59.46
Deadline: Oct 17, 7:59 a.m. 3 points
Starting with the
linear_regression_features.py
template, use scikit-learn
to train a model of a 1D curve.
Try using a concatenation of features $x^1, x^2, …, x^D$ for $D$ from 1 to a given range, and report RMSE of every such configuration.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 linear_regression_features.py --data_size=10 --test_size=5 --range=6
Maximum feature order 1: 0.74 RMSE
Maximum feature order 2: 1.87 RMSE
Maximum feature order 3: 0.53 RMSE
Maximum feature order 4: 4.52 RMSE
Maximum feature order 5: 1.70 RMSE
Maximum feature order 6: 2.82 RMSE
python3 linear_regression_features.py --data_size=30 --test_size=20 --range=9
Maximum feature order 1: 0.56 RMSE
Maximum feature order 2: 1.53 RMSE
Maximum feature order 3: 1.10 RMSE
Maximum feature order 4: 0.28 RMSE
Maximum feature order 5: 1.60 RMSE
Maximum feature order 6: 3.09 RMSE
Maximum feature order 7: 3.92 RMSE
Maximum feature order 8: 65.11 RMSE
Maximum feature order 9: 3886.97 RMSE
python3 linear_regression_features.py --data_size=50 --test_size=40 --range=9
Maximum feature order 1: 0.63 RMSE
Maximum feature order 2: 0.73 RMSE
Maximum feature order 3: 0.31 RMSE
Maximum feature order 4: 0.26 RMSE
Maximum feature order 5: 1.22 RMSE
Maximum feature order 6: 0.69 RMSE
Maximum feature order 7: 2.39 RMSE
Maximum feature order 8: 7.28 RMSE
Maximum feature order 9: 201.70 RMSE
Deadline: Oct 24, 7:59 a.m. 2 points
Starting with the linear_regression_l2.py
template, use scikit-learn
to train L2-regularized linear regression models
and print the results of the best of them.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 linear_regression_l2.py --test_size=0.15
0.49 52.11
python3 linear_regression_l2.py --test_size=0.80
0.10 53.53
Deadline: Oct 24, 7:59 a.m. 4 points
Starting with the linear_regression_sgd.py, implement minibatch SGD for linear regression and compare the results to an explicit linear regression solver.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 linear_regression_sgd.py --batch_size=10 --epochs=50 --learning_rate=0.01
Test RMSE: SGD 114.118, explicit 115.6
Learned weights: 6.864 6.907 -1.208 -2.252 -1.464 -13.323 13.909 4.883 -11.468 -0.229 37.803 -5.191 ...
python3 linear_regression_sgd.py --batch_size=10 --epochs=50 --learning_rate=0.1
Test RMSE: SGD 111.395, explicit 115.6
Learned weights: 11.559 12.428 -1.529 -2.236 -1.575 -8.868 18.842 3.882 -7.175 -1.373 38.918 -6.522 ...
python3 linear_regression_sgd.py --batch_size=10 --epochs=50 --learning_rate=0.001
Test RMSE: SGD 151.210, explicit 115.6
Learned weights: 1.885 -0.580 -0.386 0.389 -1.745 -6.994 6.787 3.019 -8.013 0.353 15.712 -3.322 ...
python3 linear_regression_sgd.py --batch_size=1 --epochs=50 --learning_rate=0.01
Test RMSE: SGD 111.395, explicit 115.6
Learned weights: 11.559 12.429 -1.529 -2.236 -1.574 -8.868 18.843 3.882 -7.174 -1.373 38.917 -6.522 ...
python3 linear_regression_sgd.py --batch_size=50 --epochs=50 --learning_rate=0.01
Test RMSE: SGD 136.015, explicit 115.6
Learned weights: 2.940 0.504 -0.555 0.143 -2.088 -10.664 9.146 4.607 -11.620 0.129 24.294 -4.089 ...
python3 linear_regression_sgd.py --batch_size=50 --epochs=500 --learning_rate=0.01
Test RMSE: SGD 111.914, explicit 115.6
Learned weights: 9.360 9.428 -1.333 -2.646 -1.379 -11.248 16.352 4.153 -9.041 -0.755 38.872 -5.881 ...
python3 linear_regression_sgd.py --batch_size=50 --epochs=500 --learning_rate=0.01 --l2=0.1
Test RMSE: SGD 113.521, explicit 115.6
Learned weights: 8.013 7.818 -1.227 -2.234 -1.491 -11.592 14.863 4.343 -9.807 -0.575 36.745 -5.487 ...
Deadline: Oct 24, 7:59 a.m. 3 points
Starting with the feature_engineering.py
template, learn how to perform basic feature engineering using scikit-learn
.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 feature_engineering.py --dataset=diabetes
-0.5745 -0.9514 1.797 -0.4984 0.4751 0.9487 -0.6961 0.7574 0.06019 1.625 0.33 0.5465 -1.033 0.2863 -0.2729 -0.545 0.3999 -0.4351 -0.03458 -0.9334 0.9052 -1.71 0.4742 -0.452 -0.9026 0.6623 -0.7206 -0.05727 -1.546 3.23 -0.8959 0.8539 1.705 -1.251 1.361 0.1082 2.92 0.2484 -0.2368 -0.4729 0.347 -0.3775 -0.03 -0.8099 0.2257 0.4507 -0.3307 0.3598 0.0286 0.7719 0.9 -0.6604 0.7185 0.0571 1.541 0.4845 -0.5272 -0.0419 -1.131 0.5736 0.04559 1.231 0.003623 0.0978 2.64
0.2776 -0.9514 0.08366 -1.148 -1.592 -1.397 -0.4687 -0.7816 -0.3766 -1.973 0.07706 -0.2641 0.02322 -0.3186 -0.442 -0.3878 -0.1301 -0.217 -0.1045 -0.5477 0.9052 -0.0796 1.092 1.515 1.329 0.4459 0.7436 0.3583 1.877 0.007 -0.09602 -0.1332 -0.1169 -0.03921 -0.06539 -0.03151 -0.1651 1.317 1.827 1.603 0.5379 0.8971 0.4322 2.264 2.535 2.224 0.7462 1.245 0.5996 3.141 1.952 0.6548 1.092 0.5261 2.757 0.2197 0.3663 0.1765 0.9247 0.6109 0.2944 1.542 0.1418 0.7431 3.893
0.8198 1.051 -0.683 -0.8108 -0.6896 -0.4871 -0.2413 -0.03186 -0.2682 0.04527 0.6721 0.8617 -0.5599 -0.6647 -0.5653 -0.3993 -0.1978 -0.02612 -0.2199 0.03711 1.105 -0.7179 -0.8522 -0.7248 -0.512 -0.2536 -0.03348 -0.2819 0.04758 0.4665 0.5538 0.471 0.3327 0.1648 0.02176 0.1832 -0.03092 0.6574 0.5591 0.3949 0.1956 0.02583 0.2175 -0.0367 0.4755 0.3359 0.1664 0.02197 0.185 -0.03121 0.2373 0.1175 0.01552 0.1306 -0.02205 0.05822 0.007686 0.06472 -0.01092 0.001015 0.008544 -0.001442 0.07194 -0.01214 0.002049
0.9747 1.051 1.211 0.6803 0.6207 -0.9859 -1.151 1.547 2.783 2.853 0.9501 1.025 1.18 0.6631 0.605 -0.961 -1.122 1.508 2.712 2.781 1.105 1.273 0.715 0.6524 -1.036 -1.21 1.626 2.925 2.999 1.467 0.8239 0.7517 -1.194 -1.394 1.873 3.37 3.456 0.4628 0.4222 -0.6707 -0.7829 1.052 1.893 1.941 0.3852 -0.6119 -0.7143 0.96 1.727 1.771 0.972 1.135 -1.525 -2.743 -2.813 1.325 -1.78 -3.203 -3.284 2.392 4.304 4.413 7.743 7.94 8.142
-0.1872 -0.9514 0.1739 -1.171 -0.5149 -0.8915 0.5925 -0.8211 0.3554 -0.1302 0.03503 0.1781 -0.03254 0.2193 0.09637 0.1669 -0.1109 0.1537 -0.06651 0.02438 0.9052 -0.1654 1.115 0.4899 0.8482 -0.5637 0.7812 -0.3381 0.1239 0.03023 -0.2037 -0.08952 -0.155 0.103 -0.1428 0.06178 -0.02264 1.372 0.6032 1.044 -0.6941 0.9619 -0.4163 0.1526 0.2651 0.459 -0.3051 0.4228 -0.183 0.06706 0.7948 -0.5282 0.732 -0.3168 0.1161 0.3511 -0.4865 0.2106 -0.07717 0.6742 -0.2918 0.1069 0.1263 -0.04628 0.01696
0.9747 -0.9514 -0.1869 -0.3058 2.659 2.728 0.3651 0.7574 0.676 -0.1302 0.9501 -0.9274 -0.1822 -0.2981 2.592 2.659 0.3559 0.7382 0.6589 -0.1269 0.9052 0.1778 0.291 -2.53 -2.596 -0.3474 -0.7206 -0.6431 0.1239 0.03494 0.05717 -0.497 -0.51 -0.06824 -0.1416 -0.1264 0.02434 0.09354 -0.8132 -0.8344 -0.1117 -0.2316 -0.2067 0.03983 7.07 7.254 0.9708 2.014 1.797 -0.3463 7.443 0.9961 2.066 1.844 -0.3553 0.1333 0.2765 0.2468 -0.04755 0.5736 0.512 -0.09864 0.457 -0.08804 0.01696
1.982 -0.9514 0.715 0.4877 -0.5149 -0.3253 -0.01389 -0.8211 -0.4683 -0.4813 3.927 -1.885 1.417 0.9664 -1.02 -0.6447 -0.02753 -1.627 -0.928 -0.9537 0.9052 -0.6803 -0.464 0.4899 0.3095 0.01322 0.7812 0.4455 0.4579 0.5113 0.3487 -0.3682 -0.2326 -0.009932 -0.5871 -0.3348 -0.3441 0.2378 -0.2511 -0.1586 -0.006774 -0.4004 -0.2284 -0.2347 0.2651 0.1675 0.007152 0.4228 0.2411 0.2478 0.1058 0.004519 0.2671 0.1523 0.1566 0.000193 0.01141 0.006505 0.006685 0.6742 0.3845 0.3952 0.2193 0.2254 0.2316
1.362 1.051 -0.1418 -0.2337 2.193 1.084 1.123 -0.03186 1.76 -0.3935 1.855 1.432 -0.1932 -0.3183 2.987 1.476 1.53 -0.04339 2.397 -0.536 1.105 -0.1491 -0.2456 2.305 1.139 1.18 -0.03348 1.85 -0.4136 0.02011 0.03314 -0.311 -0.1537 -0.1593 0.004518 -0.2496 0.05581 0.05462 -0.5125 -0.2532 -0.2625 0.007445 -0.4113 0.09196 4.809 2.376 2.463 -0.06986 3.86 -0.8629 1.174 1.217 -0.03452 1.907 -0.4264 1.261 -0.03578 1.977 -0.4419 0.001015 -0.05607 0.01253 3.098 -0.6926 0.1548
2.059 -0.9514 1.031 1.69 1.174 0.8206 -1.606 3.046 2.055 1.274 4.24 -1.959 2.122 3.48 2.417 1.69 -3.306 6.273 4.231 2.623 0.9052 -0.9806 -1.608 -1.117 -0.7807 1.528 -2.898 -1.955 -1.212 1.062 1.742 1.21 0.8458 -1.655 3.14 2.118 1.313 2.857 1.984 1.387 -2.714 5.149 3.473 2.153 1.378 0.9633 -1.885 3.576 2.412 1.495 0.6734 -1.318 2.5 1.686 1.045 2.578 -4.891 -3.3 -2.045 9.279 6.26 3.88 4.223 2.618 1.623
0.2776 1.051 -0.48 -0.0173 0.8245 1.178 -0.1655 0.7574 -0.1065 -0.218 0.07706 0.2918 -0.1333 -0.004801 0.2289 0.327 -0.04594 0.2102 -0.02956 -0.06051 1.105 -0.5046 -0.01818 0.8666 1.238 -0.1739 0.7961 -0.1119 -0.2291 0.2304 0.008303 -0.3958 -0.5654 0.07944 -0.3636 0.05111 0.1046 0.0002992 -0.01426 -0.02037 0.002862 -0.0131 0.001842 0.00377 0.6798 0.9711 -0.1364 0.6245 -0.08779 -0.1797 1.387 -0.1949 0.8921 -0.1254 -0.2568 0.02739 -0.1253 0.01762 0.03608 0.5736 -0.08064 -0.1651 0.01134 0.02321 0.04752
python3 feature_engineering.py --dataset=linnerud
1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
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python3 feature_engineering.py --dataset=wine
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Deadline: Oct 24, 7:59 a.m. 3 points+4 bonus
This assignment is a competition task. Your goal is to perform regression on the data from a bike rental shop. The train set contains 1000 instances, each instance consists of 12 features, both integral and real.
The rental_competition.py template shows how to load the training data, downloading it if needed. Furthermore, it shows how to save a trained estimator and how to load it during prediction.
The performance of your system is measured using root mean squared error and your goal is to achieve RMSE less than 100. Note that you can use any number of generalized linear models from sklearn to solve this assignment (but no decision trees, MLPs, …).
Deadline: Oct 31, 7:59 a.m. 2 points
Starting with the perceptron.py template, implement the perceptron algorithm.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 perceptron.py --data_size=100 --seed=17
Learned weights 4.10 2.94 -1.00
python3 perceptron.py --data_size=50 --seed=320
Learned weights -2.30 -1.96 -2.00
python3 perceptron.py --data_size=200 --seed=92
Learned weights 4.43 1.54 -2.00
Deadline: Oct 31, 7:59 a.m. 5 points
Starting with the logistic_regression_sgd.py, implement minibatch SGD for logistic regression.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 logistic_regression_sgd.py --data_size=100 --batch_size=10 --epochs=9 --learning_rate=0.5
After epoch 1: train loss 0.3259 acc 94.0%, test loss 0.3301 acc 96.0%
After epoch 2: train loss 0.2321 acc 96.0%, test loss 0.2385 acc 98.0%
After epoch 3: train loss 0.1877 acc 98.0%, test loss 0.1949 acc 98.0%
After epoch 4: train loss 0.1612 acc 98.0%, test loss 0.1689 acc 98.0%
After epoch 5: train loss 0.1435 acc 98.0%, test loss 0.1517 acc 98.0%
After epoch 6: train loss 0.1307 acc 98.0%, test loss 0.1396 acc 98.0%
After epoch 7: train loss 0.1208 acc 98.0%, test loss 0.1304 acc 96.0%
After epoch 8: train loss 0.1129 acc 98.0%, test loss 0.1230 acc 96.0%
After epoch 9: train loss 0.1065 acc 98.0%, test loss 0.1170 acc 96.0%
Learned weights 2.77 -0.60 0.12
python3 logistic_regression_sgd.py --data_size=95 --test_size=45 --batch_size=5 --epochs=9 --learning_rate=0.5
After epoch 1: train loss 0.2429 acc 96.0%, test loss 0.3187 acc 93.3%
After epoch 2: train loss 0.1853 acc 96.0%, test loss 0.2724 acc 93.3%
After epoch 3: train loss 0.1590 acc 96.0%, test loss 0.2525 acc 93.3%
After epoch 4: train loss 0.1428 acc 96.0%, test loss 0.2411 acc 93.3%
After epoch 5: train loss 0.1313 acc 98.0%, test loss 0.2335 acc 93.3%
After epoch 6: train loss 0.1225 acc 96.0%, test loss 0.2258 acc 93.3%
After epoch 7: train loss 0.1159 acc 96.0%, test loss 0.2220 acc 93.3%
After epoch 8: train loss 0.1105 acc 96.0%, test loss 0.2187 acc 93.3%
After epoch 9: train loss 0.1061 acc 96.0%, test loss 0.2163 acc 93.3%
Learned weights -0.61 3.61 0.12
python3 logistic_regression_sgd.py --data_size=95 --test_size=45 --batch_size=1 --epochs=9 --learning_rate=0.7
After epoch 1: train loss 0.1141 acc 96.0%, test loss 0.2268 acc 93.3%
After epoch 2: train loss 0.0867 acc 96.0%, test loss 0.2150 acc 91.1%
After epoch 3: train loss 0.0797 acc 98.0%, test loss 0.2320 acc 88.9%
After epoch 4: train loss 0.0753 acc 96.0%, test loss 0.2224 acc 88.9%
After epoch 5: train loss 0.0692 acc 96.0%, test loss 0.2154 acc 88.9%
After epoch 6: train loss 0.0749 acc 98.0%, test loss 0.2458 acc 88.9%
After epoch 7: train loss 0.0638 acc 96.0%, test loss 0.2190 acc 88.9%
After epoch 8: train loss 0.0644 acc 98.0%, test loss 0.2341 acc 88.9%
After epoch 9: train loss 0.0663 acc 98.0%, test loss 0.2490 acc 88.9%
Learned weights -1.07 7.33 -0.40
Deadline: Oct 31, 7:59 a.m. 2 points
Starting with grid_search.py
template, perform a hyperparameter grid search, evaluating hyperparameter performance
using a stratified k-fold crossvalidation, and finally evaluate a model
trained with best hyperparameters on all training data. The easiest way is
to utilize sklearn.model_selection.GridSearchCV
.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 grid_search.py --test_size=0.5
Test accuracy: 98.33%
python3 grid_search.py --test_size=0.7
Test accuracy: 96.98%
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 grid_search.py --test_size=0.5
Rank: 11 Cross-val: 86.7% lr__C: 0.01 lr__solver: lbfgs polynomial__degree: 1
Rank: 5 Cross-val: 92.7% lr__C: 0.01 lr__solver: lbfgs polynomial__degree: 2
Rank: 11 Cross-val: 86.7% lr__C: 0.01 lr__solver: sag polynomial__degree: 1
Rank: 5 Cross-val: 92.7% lr__C: 0.01 lr__solver: sag polynomial__degree: 2
Rank: 7 Cross-val: 90.8% lr__C: 1 lr__solver: lbfgs polynomial__degree: 1
Rank: 3 Cross-val: 96.8% lr__C: 1 lr__solver: lbfgs polynomial__degree: 2
Rank: 7 Cross-val: 90.8% lr__C: 1 lr__solver: sag polynomial__degree: 1
Rank: 4 Cross-val: 96.8% lr__C: 1 lr__solver: sag polynomial__degree: 2
Rank: 10 Cross-val: 90.1% lr__C: 100 lr__solver: lbfgs polynomial__degree: 1
Rank: 1 Cross-val: 97.2% lr__C: 100 lr__solver: lbfgs polynomial__degree: 2
Rank: 9 Cross-val: 90.5% lr__C: 100 lr__solver: sag polynomial__degree: 1
Rank: 2 Cross-val: 97.0% lr__C: 100 lr__solver: sag polynomial__degree: 2
Test accuracy: 98.33%
python3 grid_search.py --test_size=0.7
Rank: 11 Cross-val: 87.9% lr__C: 0.01 lr__solver: lbfgs polynomial__degree: 1
Rank: 5 Cross-val: 91.8% lr__C: 0.01 lr__solver: lbfgs polynomial__degree: 2
Rank: 11 Cross-val: 87.9% lr__C: 0.01 lr__solver: sag polynomial__degree: 1
Rank: 5 Cross-val: 91.8% lr__C: 0.01 lr__solver: sag polynomial__degree: 2
Rank: 7 Cross-val: 91.3% lr__C: 1 lr__solver: lbfgs polynomial__degree: 1
Rank: 3 Cross-val: 95.9% lr__C: 1 lr__solver: lbfgs polynomial__degree: 2
Rank: 7 Cross-val: 91.3% lr__C: 1 lr__solver: sag polynomial__degree: 1
Rank: 4 Cross-val: 95.7% lr__C: 1 lr__solver: sag polynomial__degree: 2
Rank: 10 Cross-val: 89.2% lr__C: 100 lr__solver: lbfgs polynomial__degree: 1
Rank: 1 Cross-val: 96.5% lr__C: 100 lr__solver: lbfgs polynomial__degree: 2
Rank: 9 Cross-val: 89.2% lr__C: 100 lr__solver: sag polynomial__degree: 1
Rank: 2 Cross-val: 96.1% lr__C: 100 lr__solver: sag polynomial__degree: 2
Test accuracy: 96.98%
Deadline: Oct 31, 7:59 a.m. 3 points+4 bonus
This assignment is a competition task. Your goal is to perform binary classification – given medical data with 15 binary and 6 real-valued attributes, predict whether thyroid is functioning normally or not. The train set and test set consist of ~3.5k instances.
The thyroid_competition.py template shows how to load training data, downloading it if needed. Furthermore, it shows how to save a trained estimator and how to load it during prediction.
The performance of your system is measured using accuracy of correctly predicted examples and your goal is to achieve at least 96% accuracy. Note that you can use any number of generalized linear models from sklearn to solve this assignment (but no decision trees, MLPs, …).
Deadline: Nov 7, 7:59 a.m. 3 points
Starting with the softmax_classification_sgd.py, implement minibatch SGD for multinomial logistic regression.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 softmax_classification_sgd.py --batch_size=10 --epochs=2 --learning_rate=0.005
After epoch 1: train loss 0.3130 acc 90.8%, test loss 0.3529 acc 88.7%
After epoch 2: train loss 0.2134 acc 93.9%, test loss 0.2450 acc 92.5%
Learned weights:
-0.03 -0.10 0.01 0.06 -0.07 0.04 -0.05 0.05 0.07 -0.10 ...
0.09 0.08 -0.12 -0.08 -0.10 0.09 -0.03 -0.06 0.02 -0.01 ...
0.05 0.07 0.01 -0.03 -0.05 0.06 0.04 -0.10 -0.03 0.08 ...
0.02 -0.05 -0.01 0.10 0.11 0.09 -0.05 0.06 -0.09 0.04 ...
-0.07 -0.07 -0.10 -0.01 -0.06 -0.07 -0.08 0.04 -0.04 0.01 ...
-0.07 -0.05 0.14 0.06 0.02 0.14 0.05 0.04 -0.04 0.03 ...
-0.09 -0.04 -0.11 -0.06 -0.04 -0.10 -0.09 0.05 0.05 -0.01 ...
0.07 0.01 0.02 -0.04 0.04 -0.01 0.11 -0.06 0.03 -0.03 ...
0.02 -0.02 0.01 -0.03 0.00 -0.03 -0.09 -0.03 0.08 -0.07 ...
0.04 -0.04 -0.05 0.05 -0.04 -0.05 0.09 -0.08 -0.01 -0.04 ...
python3 softmax_classification_sgd.py --batch_size=1 --epochs=1 --learning_rate=0.005 --test_size=1597
After epoch 1: train loss 1.7683 acc 73.5%, test loss 2.0028 acc 72.2%
Learned weights:
-0.03 -0.10 0.03 0.08 0.03 0.03 -0.07 0.05 0.07 -0.15 ...
0.09 0.08 -0.25 -0.15 -0.17 0.11 -0.00 -0.06 0.02 -0.05 ...
0.05 0.06 0.07 0.04 -0.12 0.11 0.07 -0.10 -0.03 0.16 ...
0.02 -0.03 0.03 0.14 0.03 0.08 -0.09 0.06 -0.09 0.09 ...
-0.07 -0.08 -0.22 -0.07 -0.11 -0.27 -0.13 0.04 -0.04 -0.00 ...
-0.07 -0.08 0.17 0.16 0.17 0.39 0.07 0.04 -0.03 0.03 ...
-0.09 -0.04 -0.13 -0.10 -0.03 -0.16 -0.09 0.05 0.05 -0.03 ...
0.07 0.02 0.10 0.03 0.09 -0.05 0.13 -0.08 0.03 -0.05 ...
0.02 0.00 0.02 -0.17 -0.01 -0.04 -0.12 -0.03 0.08 -0.09 ...
0.04 -0.04 -0.02 0.06 -0.07 -0.05 0.16 -0.08 -0.01 -0.01 ...
python3 softmax_classification_sgd.py --batch_size=100 --epochs=3 --learning_rate=0.05
After epoch 1: train loss 4.2661 acc 77.8%, test loss 4.4413 acc 75.5%
After epoch 2: train loss 0.4290 acc 90.5%, test loss 0.5414 acc 89.8%
After epoch 3: train loss 0.6189 acc 88.0%, test loss 0.5752 acc 89.2%
Learned weights:
-0.03 -0.10 -0.04 0.08 -0.07 -0.02 -0.05 0.05 0.07 -0.12 ...
0.09 0.06 -0.23 -0.08 -0.12 0.11 -0.04 -0.06 0.02 -0.09 ...
0.05 0.09 0.07 -0.01 -0.08 0.01 0.02 -0.10 -0.03 0.16 ...
0.02 -0.04 0.03 0.15 0.18 0.09 -0.09 0.06 -0.09 0.10 ...
-0.07 -0.07 -0.14 -0.07 -0.07 -0.13 -0.11 0.03 -0.04 -0.01 ...
-0.07 -0.03 0.28 0.07 0.06 0.29 0.11 0.04 -0.03 0.08 ...
-0.09 -0.04 -0.16 -0.09 -0.04 -0.13 -0.09 0.05 0.04 -0.05 ...
0.07 0.01 0.06 -0.01 0.05 0.06 0.18 -0.04 0.03 -0.03 ...
0.02 -0.03 -0.03 -0.08 -0.00 -0.03 -0.12 -0.04 0.08 -0.12 ...
0.04 -0.05 -0.04 0.06 -0.09 -0.09 0.12 -0.09 -0.01 -0.03 ...
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 softmax_classification_sgd.py --batch_size=10 --epochs=10 --learning_rate=0.005
After epoch 1: train loss 0.3130 acc 90.8%, test loss 0.3529 acc 88.7%
After epoch 2: train loss 0.2134 acc 93.9%, test loss 0.2450 acc 92.5%
After epoch 3: train loss 0.1366 acc 96.8%, test loss 0.1735 acc 94.6%
After epoch 4: train loss 0.1374 acc 96.2%, test loss 0.1705 acc 94.0%
After epoch 5: train loss 0.1169 acc 97.2%, test loss 0.1667 acc 95.1%
After epoch 6: train loss 0.0978 acc 97.5%, test loss 0.1340 acc 96.1%
After epoch 7: train loss 0.0878 acc 98.0%, test loss 0.1366 acc 95.9%
After epoch 8: train loss 0.0889 acc 97.5%, test loss 0.1515 acc 95.1%
After epoch 9: train loss 0.0819 acc 98.0%, test loss 0.1336 acc 96.5%
After epoch 10: train loss 0.0801 acc 97.9%, test loss 0.1342 acc 96.4%
Learned weights:
-0.03 -0.10 0.01 0.08 -0.05 0.01 -0.06 0.05 0.07 -0.10 ...
0.09 0.07 -0.15 -0.02 -0.21 0.13 -0.01 -0.06 0.02 -0.07 ...
0.05 0.08 0.01 -0.03 -0.05 0.06 0.04 -0.10 -0.03 0.09 ...
0.02 -0.03 -0.02 0.11 0.16 0.09 -0.06 0.06 -0.09 0.05 ...
-0.07 -0.07 -0.10 -0.07 -0.10 -0.13 -0.09 0.03 -0.04 0.02 ...
-0.07 -0.04 0.20 0.05 -0.02 0.12 0.06 0.04 -0.04 0.01 ...
-0.09 -0.04 -0.14 -0.09 -0.02 -0.08 -0.09 0.05 0.05 -0.03 ...
0.07 0.01 0.05 -0.01 0.06 -0.01 0.13 -0.04 0.03 -0.02 ...
0.02 -0.02 0.01 -0.08 0.03 0.01 -0.10 -0.03 0.08 -0.05 ...
0.04 -0.05 -0.07 0.09 -0.00 -0.05 0.10 -0.09 -0.01 0.01 ...
python3 softmax_classification_sgd.py --batch_size=1 --epochs=10 --learning_rate=0.005 --test_size=1597
After epoch 1: train loss 1.7683 acc 73.5%, test loss 2.0028 acc 72.2%
After epoch 2: train loss 0.7731 acc 88.5%, test loss 1.5357 acc 77.8%
After epoch 3: train loss 1.2056 acc 82.5%, test loss 2.0547 acc 73.7%
After epoch 4: train loss 1.2783 acc 88.0%, test loss 2.4955 acc 78.4%
After epoch 5: train loss 0.1834 acc 97.5%, test loss 0.8377 acc 87.8%
After epoch 6: train loss 0.5430 acc 93.0%, test loss 1.4756 acc 83.5%
After epoch 7: train loss 0.0666 acc 98.5%, test loss 0.6144 acc 89.6%
After epoch 8: train loss 0.1177 acc 95.5%, test loss 1.3177 acc 81.7%
After epoch 9: train loss 0.0033 acc 100.0%, test loss 0.6159 acc 90.9%
After epoch 10: train loss 0.0214 acc 99.0%, test loss 0.6789 acc 90.5%
Learned weights:
-0.03 -0.10 0.04 0.14 0.09 -0.00 -0.07 0.05 0.07 -0.16 ...
0.09 0.11 -0.24 -0.14 -0.47 0.17 -0.12 -0.06 0.02 -0.11 ...
0.05 0.07 0.09 -0.01 -0.12 0.03 0.03 -0.10 -0.04 0.31 ...
0.02 -0.01 0.13 0.28 0.14 0.15 -0.12 0.06 -0.08 0.18 ...
-0.07 -0.09 -0.08 -0.33 -0.23 -0.52 -0.22 0.04 -0.04 0.04 ...
-0.07 -0.08 0.37 0.18 0.11 0.49 0.10 0.04 -0.03 0.13 ...
-0.09 -0.06 -0.35 -0.17 0.11 -0.02 -0.10 0.05 0.05 -0.13 ...
0.07 0.02 0.06 0.08 0.17 0.10 0.25 -0.08 0.03 -0.08 ...
0.02 -0.02 -0.10 -0.06 0.04 -0.12 -0.12 -0.03 0.08 -0.21 ...
0.04 -0.04 -0.13 0.04 -0.03 -0.12 0.30 -0.08 -0.01 -0.08 ...
python3 softmax_classification_sgd.py --batch_size=100 --epochs=10 --learning_rate=0.05
After epoch 1: train loss 4.2661 acc 77.8%, test loss 4.4413 acc 75.5%
After epoch 2: train loss 0.4290 acc 90.5%, test loss 0.5414 acc 89.8%
After epoch 3: train loss 0.6189 acc 88.0%, test loss 0.5752 acc 89.2%
After epoch 4: train loss 0.3084 acc 91.9%, test loss 0.3482 acc 91.3%
After epoch 5: train loss 0.2757 acc 93.2%, test loss 0.3792 acc 91.3%
After epoch 6: train loss 0.2559 acc 92.7%, test loss 0.3718 acc 91.8%
After epoch 7: train loss 0.1164 acc 96.8%, test loss 0.1761 acc 95.1%
After epoch 8: train loss 0.2891 acc 91.5%, test loss 0.4110 acc 90.2%
After epoch 9: train loss 0.1256 acc 96.4%, test loss 0.1977 acc 94.9%
After epoch 10: train loss 0.1239 acc 96.3%, test loss 0.1847 acc 95.0%
Learned weights:
-0.03 -0.10 -0.05 0.07 -0.08 -0.04 -0.06 0.05 0.07 -0.12 ...
0.09 0.05 -0.24 -0.03 -0.25 0.16 -0.01 -0.06 0.02 -0.13 ...
0.05 0.10 0.05 -0.02 -0.06 0.04 0.03 -0.10 -0.03 0.16 ...
0.02 -0.03 0.03 0.15 0.25 0.13 -0.09 0.06 -0.09 0.11 ...
-0.07 -0.08 -0.13 -0.10 -0.10 -0.18 -0.11 0.03 -0.04 0.00 ...
-0.07 -0.02 0.32 0.06 0.03 0.23 0.10 0.04 -0.03 0.03 ...
-0.09 -0.04 -0.18 -0.12 -0.01 -0.12 -0.10 0.05 0.04 -0.06 ...
0.07 0.01 0.10 0.00 0.05 0.05 0.20 -0.02 0.03 -0.02 ...
0.02 -0.03 -0.04 -0.12 0.02 -0.02 -0.15 -0.04 0.08 -0.08 ...
0.04 -0.06 -0.06 0.12 -0.04 -0.10 0.12 -0.09 -0.01 0.01 ...
Deadline: Nov 7 Nov 14, 7:59 a.m.
6 points
Starting with the mlp_classification_sgd.py, implement minibatch SGD for multilayer perceptron classification.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 mlp_classification_sgd.py --epochs=3 --batch_size=10 --hidden_layer=20
After epoch 1: train acc 79.7%, test acc 80.2%
After epoch 2: train acc 91.9%, test acc 88.3%
After epoch 3: train acc 92.4%, test acc 90.0%
Learned parameters:
-0.03 0.09 0.05 0.02 -0.07 -0.07 -0.09 0.07 0.02 0.04 -0.10 0.09 ...
-0.09 0.07 0.21 -0.16 -0.15 -0.07 0.01 -0.09 0.05 -0.11 -0.02 -0.04 ...
-0.00 -0.00 0.00 0.00 0.00 0.00 -0.00 0.00 0.00 -0.00 0.00 -0.00 ...
0.01 -0.01 0.01 -0.02 0.01 -0.01 0.00 0.01 0.01 -0.01 ...
python3 mlp_classification_sgd.py --epochs=3 --batch_size=10 --hidden_layer=50
After epoch 1: train acc 91.1%, test acc 89.2%
After epoch 2: train acc 95.9%, test acc 93.5%
After epoch 3: train acc 96.5%, test acc 95.2%
Learned parameters:
-0.03 0.09 0.05 0.02 -0.07 -0.07 -0.09 0.07 0.02 0.04 -0.10 0.09 ...
0.01 0.06 -0.13 0.04 0.11 0.04 0.13 0.01 0.05 -0.05 -0.07 0.02 ...
0.00 0.00 -0.00 0.00 -0.00 0.00 -0.00 0.00 0.00 -0.00 -0.00 -0.00 ...
0.01 0.00 -0.00 0.00 -0.00 0.00 -0.01 -0.00 -0.00 0.00 ...
python3 mlp_classification_sgd.py --epochs=3 --batch_size=10 --hidden_layer=200
After epoch 1: train acc 95.4%, test acc 93.0%
After epoch 2: train acc 97.9%, test acc 96.6%
After epoch 3: train acc 98.8%, test acc 96.9%
Learned parameters:
-0.03 0.09 0.05 0.02 -0.07 -0.07 -0.09 0.07 0.02 0.04 -0.10 0.09 ...
0.01 -0.09 0.04 -0.09 0.06 0.06 -0.05 -0.04 -0.00 0.02 -0.04 0.02 ...
0.00 0.00 -0.00 -0.00 0.00 -0.00 0.00 -0.00 0.00 0.00 -0.00 -0.00 ...
-0.00 -0.00 -0.00 -0.00 0.00 -0.00 -0.00 0.00 0.00 0.00 ...
python3 mlp_classification_sgd.py --epochs=1 --batch_size=1 --hidden_layer=200 --test_size=1597
After epoch 1: train acc 74.0%, test acc 68.7%
Learned parameters:
-0.03 0.09 0.05 0.02 -0.07 -0.07 -0.09 0.07 0.02 0.04 -0.10 0.09 ...
0.01 -0.09 0.04 -0.09 0.06 0.06 -0.05 -0.04 -0.00 0.02 -0.04 0.02 ...
0.00 0.00 -0.00 -0.00 -0.00 -0.00 -0.00 -0.00 0.00 -0.00 -0.00 -0.00 ...
-0.02 0.01 -0.00 -0.02 0.02 -0.00 0.00 -0.02 0.02 0.01 ...
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 mlp_classification_sgd.py --epochs=10 --batch_size=10 --hidden_layer=20
After epoch 1: train acc 79.7%, test acc 80.2%
After epoch 2: train acc 91.9%, test acc 88.3%
After epoch 3: train acc 92.4%, test acc 90.0%
After epoch 4: train acc 96.1%, test acc 93.1%
After epoch 5: train acc 95.3%, test acc 93.1%
After epoch 6: train acc 96.6%, test acc 93.9%
After epoch 7: train acc 97.3%, test acc 94.2%
After epoch 8: train acc 98.2%, test acc 94.9%
After epoch 9: train acc 98.1%, test acc 95.7%
After epoch 10: train acc 97.4%, test acc 95.1%
Learned parameters:
-0.03 0.09 0.05 0.02 -0.07 -0.07 -0.09 0.07 0.02 0.04 -0.10 0.09 ...
-0.07 0.12 0.33 -0.21 -0.16 -0.13 0.02 -0.14 0.01 -0.12 -0.02 -0.04 ...
-0.00 -0.00 0.00 -0.00 0.00 0.00 -0.00 0.00 0.00 -0.00 0.00 0.00 ...
0.02 -0.01 0.01 -0.03 0.02 -0.01 0.00 0.01 0.01 -0.01 ...
python3 mlp_classification_sgd.py --epochs=10 --batch_size=10 --hidden_layer=50
After epoch 1: train acc 91.1%, test acc 89.2%
After epoch 2: train acc 95.9%, test acc 93.5%
After epoch 3: train acc 96.5%, test acc 95.2%
After epoch 4: train acc 96.1%, test acc 94.5%
After epoch 5: train acc 96.3%, test acc 93.5%
After epoch 6: train acc 98.3%, test acc 96.2%
After epoch 7: train acc 98.4%, test acc 96.4%
After epoch 8: train acc 98.3%, test acc 95.7%
After epoch 9: train acc 99.1%, test acc 97.4%
After epoch 10: train acc 98.8%, test acc 97.4%
Learned parameters:
-0.03 0.09 0.05 0.02 -0.07 -0.07 -0.09 0.07 0.02 0.04 -0.10 0.09 ...
0.01 0.10 -0.16 0.02 0.13 0.04 0.14 -0.01 0.05 -0.07 -0.08 0.02 ...
0.00 0.00 -0.00 0.00 0.00 -0.00 -0.00 0.00 0.00 -0.00 -0.00 -0.00 ...
0.01 -0.00 -0.00 0.00 0.00 0.00 -0.01 0.00 -0.00 -0.00 ...
python3 mlp_classification_sgd.py --epochs=10 --batch_size=10 --hidden_layer=200
After epoch 1: train acc 95.4%, test acc 93.0%
After epoch 2: train acc 97.9%, test acc 96.6%
After epoch 3: train acc 98.8%, test acc 96.9%
After epoch 4: train acc 98.0%, test acc 95.4%
After epoch 5: train acc 99.6%, test acc 97.7%
After epoch 6: train acc 99.7%, test acc 98.0%
After epoch 7: train acc 97.4%, test acc 95.4%
After epoch 8: train acc 99.7%, test acc 97.5%
After epoch 9: train acc 99.8%, test acc 97.9%
After epoch 10: train acc 99.9%, test acc 97.9%
Learned parameters:
-0.03 0.09 0.05 0.02 -0.07 -0.07 -0.09 0.07 0.02 0.04 -0.10 0.09 ...
0.01 -0.09 0.04 -0.09 0.06 0.06 -0.05 -0.04 -0.00 0.02 -0.04 0.02 ...
0.00 0.00 0.00 -0.00 0.00 -0.00 -0.00 -0.00 0.00 0.00 0.00 -0.00 ...
-0.00 -0.01 -0.00 -0.00 0.00 -0.00 -0.00 0.00 0.01 0.00 ...
Deadline: Nov 7, 7:59 a.m. 4 points+5 bonus
This assignment is a competition task. Your goal is to perform 10-class classification on the well-known MNIST dataset. The train set contains 60k images, each consisting of $28×28$ pixels with values in $\{0, 1, …, 255\}$. Evaluation is performed on 10k test images. You can find a simple online demo of a trained classifier here.
The mnist_competition.py template shows how to load training data, downloading it if needed. Furthermore, it shows how to save a trained estimator and how to load it during prediction.
The performance of your system is measured using accuracy of correctly predicted examples and your goal is to achieve at least 97% accuracy. Note that you can use any sklearn algorithm to solve this exercise (and of course anything you implement yourself).
Deadline: Nov 14, 7:59 a.m. 3 points
Starting with the multilabel_classification_sgd.py, implement minibatch SGD for multi-label classification and manually compute micro-averaged and macro-averaged $F_1$-score.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 multilabel_classification_sgd.py --batch_size=10 --epochs=2 --classes=5
After epoch 1: train F1 micro 56.45% macro 46.71%, test F1 micro 58.25% macro 43.9%
After epoch 2: train F1 micro 71.46% macro 59.47%, test F1 micro 73.77% macro 60.3%
Learned weights:
-0.05 -0.11 -0.12 -0.05 0.04 0.04 0.02 0.01 -0.05 0.03 ...
0.05 -0.01 0.09 -0.05 -0.06 -0.08 -0.05 0.02 0.03 0.00 ...
0.10 0.16 0.08 0.01 -0.02 -0.05 -0.11 -0.09 -0.04 0.05 ...
0.03 0.00 -0.06 -0.01 0.01 0.06 0.10 0.08 0.12 0.01 ...
-0.03 -0.02 -0.08 -0.05 -0.07 -0.05 0.06 -0.03 -0.09 -0.09 ...
python3 multilabel_classification_sgd.py --batch_size=10 --epochs=2 --classes=10
After epoch 1: train F1 micro 20.14% macro 9.95%, test F1 micro 21.57% macro 10.4%
After epoch 2: train F1 micro 11.29% macro 7.35%, test F1 micro 14.45% macro 8.8%
Learned weights:
-0.04 -0.09 -0.01 -0.01 -0.09 0.02 0.01 0.04 0.02 -0.11 ...
0.12 0.07 -0.09 -0.07 0.04 0.02 -0.06 -0.03 0.03 0.05 ...
0.05 0.03 -0.11 -0.13 -0.09 0.08 0.02 -0.14 -0.01 -0.00 ...
-0.03 -0.07 0.00 0.09 0.08 0.01 -0.01 -0.04 -0.08 -0.02 ...
-0.11 -0.11 -0.04 0.04 -0.11 -0.03 -0.08 -0.03 -0.07 0.03 ...
-0.11 -0.07 0.04 0.04 -0.00 0.04 0.00 -0.03 -0.06 -0.05 ...
-0.14 -0.08 -0.12 -0.09 -0.11 -0.15 -0.09 -0.01 0.01 -0.05 ...
0.04 0.00 -0.08 -0.10 -0.06 -0.04 -0.01 -0.10 -0.00 0.02 ...
0.03 0.01 0.04 0.03 -0.06 -0.10 -0.09 0.04 0.02 -0.10 ...
0.04 -0.06 -0.07 -0.03 -0.09 0.04 0.05 -0.09 -0.04 -0.10 ...
python3 multilabel_classification_sgd.py --batch_size=5 --epochs=2 --classes=5 --learning_rate=0.02
After epoch 1: train F1 micro 60.66% macro 47.96%, test F1 micro 60.82% macro 46.6%
After epoch 2: train F1 micro 79.28% macro 77.99%, test F1 micro 77.65% macro 71.1%
Learned weights:
-0.08 -0.15 -0.14 -0.01 0.09 0.03 0.04 -0.08 0.03 0.08 ...
-0.06 0.09 0.04 -0.06 -0.08 -0.13 -0.06 0.11 0.07 0.01 ...
0.21 0.28 0.12 0.03 0.02 -0.16 -0.16 -0.14 0.06 0.13 ...
0.07 -0.00 -0.04 0.00 0.12 0.13 0.11 0.19 0.21 0.03 ...
0.07 -0.10 -0.10 -0.04 -0.19 0.05 0.01 -0.03 -0.15 -0.10 ...
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 multilabel_classification_sgd.py --batch_size=10 --epochs=9 --classes=5
After epoch 1: train F1 micro 56.45% macro 46.71%, test F1 micro 58.25% macro 43.9%
After epoch 2: train F1 micro 71.46% macro 59.47%, test F1 micro 73.77% macro 60.3%
After epoch 3: train F1 micro 73.06% macro 61.02%, test F1 micro 71.71% macro 56.8%
After epoch 4: train F1 micro 77.30% macro 66.48%, test F1 micro 76.19% macro 64.1%
After epoch 5: train F1 micro 76.05% macro 67.34%, test F1 micro 74.46% macro 61.4%
After epoch 6: train F1 micro 78.22% macro 73.24%, test F1 micro 77.40% macro 66.1%
After epoch 7: train F1 micro 78.13% macro 73.33%, test F1 micro 74.41% macro 61.7%
After epoch 8: train F1 micro 78.92% macro 74.73%, test F1 micro 76.78% macro 66.9%
After epoch 9: train F1 micro 80.76% macro 76.31%, test F1 micro 78.18% macro 68.3%
Learned weights:
-0.09 -0.17 -0.16 -0.01 0.09 0.01 0.04 -0.09 0.04 0.07 ...
-0.08 0.09 0.02 -0.07 -0.08 -0.13 -0.07 0.09 0.06 0.01 ...
0.20 0.25 0.09 0.00 0.02 -0.18 -0.18 -0.15 0.06 0.07 ...
0.06 -0.04 -0.07 -0.01 0.10 0.13 0.10 0.17 0.20 -0.01 ...
0.06 -0.11 -0.12 -0.05 -0.20 0.04 -0.01 -0.03 -0.16 -0.11 ...
python3 multilabel_classification_sgd.py --batch_size=10 --epochs=9 --classes=10
After epoch 1: train F1 micro 20.14% macro 9.95%, test F1 micro 21.57% macro 10.4%
After epoch 2: train F1 micro 11.29% macro 7.35%, test F1 micro 14.45% macro 8.8%
After epoch 3: train F1 micro 41.53% macro 26.29%, test F1 micro 33.54% macro 20.4%
After epoch 4: train F1 micro 44.23% macro 30.24%, test F1 micro 37.85% macro 24.4%
After epoch 5: train F1 micro 43.23% macro 29.85%, test F1 micro 42.37% macro 28.3%
After epoch 6: train F1 micro 49.53% macro 35.63%, test F1 micro 46.53% macro 32.2%
After epoch 7: train F1 micro 55.69% macro 40.36%, test F1 micro 48.21% macro 33.8%
After epoch 8: train F1 micro 52.47% macro 37.65%, test F1 micro 46.53% macro 31.9%
After epoch 9: train F1 micro 59.89% macro 43.27%, test F1 micro 53.44% macro 37.5%
Learned weights:
-0.02 -0.04 -0.02 -0.08 -0.04 -0.10 0.12 0.04 -0.06 -0.15 ...
0.18 0.04 -0.10 -0.06 0.15 -0.06 -0.08 0.05 0.05 0.05 ...
0.13 -0.02 -0.20 -0.20 -0.01 0.13 -0.06 -0.15 0.09 -0.08 ...
-0.05 -0.08 0.11 0.12 0.13 -0.07 0.05 -0.22 -0.02 -0.02 ...
-0.09 -0.14 -0.00 -0.02 -0.10 -0.05 -0.09 -0.08 -0.06 0.07 ...
-0.10 -0.01 0.11 0.03 0.03 0.04 0.05 -0.11 -0.04 -0.10 ...
-0.16 -0.09 -0.13 -0.11 -0.10 -0.20 -0.04 -0.00 0.04 -0.08 ...
-0.03 0.05 -0.21 -0.09 -0.12 0.03 -0.13 -0.09 -0.02 0.13 ...
0.05 0.07 0.08 0.04 -0.18 -0.11 -0.09 0.18 -0.09 -0.07 ...
0.04 -0.10 0.00 -0.07 -0.15 0.17 -0.03 -0.12 -0.12 -0.16 ...
python3 multilabel_classification_sgd.py --batch_size=5 --epochs=9 --classes=5 --learning_rate=0.02
After epoch 1: train F1 micro 60.66% macro 47.96%, test F1 micro 60.82% macro 46.6%
After epoch 2: train F1 micro 79.28% macro 77.99%, test F1 micro 77.65% macro 71.1%
After epoch 3: train F1 micro 80.27% macro 74.86%, test F1 micro 79.57% macro 69.6%
After epoch 4: train F1 micro 81.22% macro 79.85%, test F1 micro 77.41% macro 70.1%
After epoch 5: train F1 micro 80.50% macro 78.76%, test F1 micro 72.54% macro 65.1%
After epoch 6: train F1 micro 82.86% macro 81.46%, test F1 micro 75.62% macro 69.2%
After epoch 7: train F1 micro 81.19% macro 79.54%, test F1 micro 72.51% macro 65.3%
After epoch 8: train F1 micro 81.37% macro 79.59%, test F1 micro 75.06% macro 68.9%
After epoch 9: train F1 micro 83.83% macro 82.38%, test F1 micro 79.74% macro 74.3%
Learned weights:
-0.18 -0.31 -0.23 0.05 0.12 -0.02 0.09 -0.25 0.21 0.16 ...
-0.21 0.18 -0.12 -0.08 -0.13 -0.17 -0.12 0.15 0.10 0.04 ...
0.47 0.32 0.13 0.01 0.09 -0.36 -0.29 -0.26 0.27 0.14 ...
0.12 -0.07 -0.11 0.04 0.28 0.21 0.11 0.28 0.39 0.04 ...
0.22 -0.24 -0.26 -0.03 -0.48 0.06 -0.10 0.01 -0.28 -0.14 ...
Deadline: Nov 14, 7:59 a.m. 5 points+5 bonus
The goal of the diacritization
competition task
is to learn to add diacritics to the given Czech text. We will use a small
collection of fiction books,
which is available under CC BY-NC-SA license.
Note that these texts are the only allowed training data, you cannot use any
other Czech texts (even manually annotated) to train or evaluate your model.
At the test time, you will be given a text without diacritics and you should return
it including diacritical marks – to be explicit, we only consider diacritized
letters áčďéěíňóřšťúůýž
and their uppercase variants.
The diacritization.py template shows how to load the training data, downloading it if needed.
Each sentence in the data is stored on a single line, with exactly one space character separating input words. The performance of your system is measured using word accuracy (the percentage of words you diacritized correctly, as computed by the diacritization_eval.py script) and your goal is to achieve at least 86.5%. You can use any sklearn algorithm with the exception of decision trees to solve this assignment (so no random forests, extra trees, gradient boosting, AdaBoost with decision trees, …).
Deadline: Nov 21, 7:59 a.m. 3 points
The template changed on Nov 8, please redownload it if you got it sooner
(the LogisticRegression
needs to get an additional C=10_000
argument).
Using the tf_idf.py
template, perform classification of text documents from the
20 Newsgroups dataset. To represent the
documents, use TF and/or IDF weights, which you implement manually (without
using the sklearn.feature_extraction
module in any way). Classify test set
documents using sklearn.linear_model.LogisticRegression
trained on the
given training data, and report macro F1-score.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 tf_idf.py --train_size=1000 --test_size=500 --seed=79
Number of unique terms with at least two occurrences: 12855
F-1 score for TF=False, IDF=False: 40.7%
python3 tf_idf.py --train_size=1000 --test_size=500 --seed=79 --tf
Number of unique terms with at least two occurrences: 12855
F-1 score for TF=True, IDF=False: 44.5%
python3 tf_idf.py --train_size=1000 --test_size=500 --seed=79 --idf
Number of unique terms with at least two occurrences: 12855
F-1 score for TF=False, IDF=True: 54.1%
python3 tf_idf.py --train_size=1000 --test_size=500 --seed=79 --tf --idf
Number of unique terms with at least two occurrences: 12855
F-1 score for TF=True, IDF=True: 61.8%
python3 tf_idf.py --train_size=3000 --test_size=500 --seed=79
Number of unique terms with at least two occurrences: 26587
F-1 score for TF=False, IDF=False: 58.4%
python3 tf_idf.py --train_size=3000 --test_size=500 --seed=79 --tf
Number of unique terms with at least two occurrences: 26587
F-1 score for TF=True, IDF=False: 64.0%
python3 tf_idf.py --train_size=3000 --test_size=500 --seed=79 --idf
Number of unique terms with at least two occurrences: 26587
F-1 score for TF=False, IDF=True: 64.7%
python3 tf_idf.py --train_size=3000 --test_size=500 --seed=79 --tf --idf
Number of unique terms with at least two occurrences: 26587
F-1 score for TF=True, IDF=True: 72.8%
Deadline: Nov 21, 7:59 a.m. 3 points
Using the imdb_sentiment.py template, perform classification of text documents from the Large Movie Review Dataset. The template also downloads pretrained word embeddings from FastText containing only words from our dataset.
Analogously to competition assignments, your task is to submit a trained model that will perform classification on the test set given to you during ReCodEx evaluation. The performance is measured using accuracy, and your goal is to achieve at least 80%.
Deadline: Nov 21, 7:59 a.m. 4 points+4 bonus
The diacritization_dictionary
is an extension of the diacritization
competition.
In addition to the original training data,
in this task you can also use a dictionary providing all known diacritized
variants
of word forms present in the training and testing data, available again under
CC BY-NC-SA license.
The dictionary is not guaranteed to contain all words from the training and
testing data, but if it contains a word, all valid Czech diacritization variants
should be present.
The rules of the competition are the same as of the diacritization
competition,
except that
The diacritization_dictionary.py
module provides a Dictionary
class, which loads the dictionary
(downloading it if necessary), exposing it in Dictionary.variants
field
as a mapping from undiacritized word form to a list of known diacritized
variants.
Note that the fiction-dictionary.txt
is available in ReCodEx during evaluation.
Deadline: Nov 28, 7:59 a.m. 3 points
Starting with the k_nearest_neighbors.py,
implement k-nearest neighbors algorithm for classifying MNIST, without using the
sklearn.neighbors
module or scipy.spatial
module in any way.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 k_nearest_neighbors.py --k=1 --p=2 --weights=uniform --test_size=500 --train_size=100
K-nn accuracy for 1 nearest neighbors, L_2 metric, uniform weights: 73.60%
python3 k_nearest_neighbors.py --k=3 --p=2 --weights=uniform --test_size=500 --train_size=100
K-nn accuracy for 3 nearest neighbors, L_2 metric, uniform weights: 66.80%
python3 k_nearest_neighbors.py --k=1 --p=2 --weights=uniform --test_size=500 --train_size=1000
K-nn accuracy for 1 nearest neighbors, L_2 metric, uniform weights: 90.40%
python3 k_nearest_neighbors.py --k=5 --p=2 --weights=uniform --test_size=500 --train_size=1000
K-nn accuracy for 5 nearest neighbors, L_2 metric, uniform weights: 88.40%
python3 k_nearest_neighbors.py --k=5 --p=1 --weights=uniform --test_size=500 --train_size=1000
K-nn accuracy for 5 nearest neighbors, L_1 metric, uniform weights: 87.00%
python3 k_nearest_neighbors.py --k=5 --p=3 --weights=uniform --test_size=500 --train_size=1000
K-nn accuracy for 5 nearest neighbors, L_3 metric, uniform weights: 89.40%
python3 k_nearest_neighbors.py --k=1 --p=2 --weights=uniform --test_size=500 --train_size=5000
K-nn accuracy for 1 nearest neighbors, L_2 metric, uniform weights: 94.40%
python3 k_nearest_neighbors.py --k=9 --p=2 --weights=uniform --test_size=500 --train_size=5000
K-nn accuracy for 9 nearest neighbors, L_2 metric, uniform weights: 92.80%
python3 k_nearest_neighbors.py --k=9 --p=2 --weights=inverse --test_size=500 --train_size=5000
K-nn accuracy for 9 nearest neighbors, L_2 metric, inverse weights: 93.00%
python3 k_nearest_neighbors.py --k=9 --p=2 --weights=softmax --test_size=500 --train_size=5000
K-nn accuracy for 9 nearest neighbors, L_2 metric, softmax weights: 94.00%
Deadline: Nov 28, 7:59 a.m. 3 points
Using the naive_bayes.py
template, implement a naive Bayes classifier (without using the
sklearn.naive_bayes
module in any way). Support all of Gaussian NB,
multinomial NB and Bernoulli NB.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 naive_bayes.py --classes=3 --naive_bayes_type=bernoulli
Test accuracy 95.17%, log probability -4933.45
python3 naive_bayes.py --classes=3 --naive_bayes_type=multinomial
Test accuracy 93.68%, log probability -300352.40
python3 naive_bayes.py --classes=3 --naive_bayes_type=gaussian
Test accuracy 95.54%, log probability -31895.82
python3 naive_bayes.py --classes=10 --naive_bayes_type=bernoulli
Test accuracy 89.21%, log probability -18342.14
python3 naive_bayes.py --classes=10 --naive_bayes_type=bernoulli --alpha=10
Test accuracy 88.54%, log probability -20829.42
python3 naive_bayes.py --classes=10 --naive_bayes_type=multinomial --alpha=10
Test accuracy 90.32%, log probability -1006524.57
python3 naive_bayes.py --classes=10 --naive_bayes_type=gaussian --alpha=10
Test accuracy 92.10%, log probability -149703.75
Deadline: Nov 28, 7:59 a.m. 4 points+4 bonus
The goal of the isnt_it_ironic
competition task is to learn to
classify given text as ironic or not.
The isnt_it_ironic.py template shows how to load the training data, downloading it if needed. Please note that the data are provided only for the purpose of this class and you cannot use them in any other way.
Each instance is a string of an English tweet. The texts have already been tokenized and tokens are separated by exactly one space. The performance of your solution will be evaluated using F1-score with sklearn.metrics.f1_score and if you surpass at least 60%, you will obtain 4 points. Note that you can use any sklearn algorithm to solve this exercise (or anything you implement yourselves).
You might find TfidfTransformer or TfidfVectorizer useful.
Deadline: Dec 5, 7:59 a.m. 3 points
Using the metric_correlation.py template, find a $\beta$ for which $F_\beta$ score correlates best with human ratings.
We use an aritificial dataset, which for every sentence contains:
Using bootstrap resampling, compute the mean human rating and $F_\beta$ score for each sampled dataset and then manually compute the Pearson correlation for betas between 0 and 2, and return the most correlating beta.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 metric_correlation.py --bootstrap_samples=100 --data_size=1000
Best correlation of 0.711 was found for beta 0.79
python3 metric_correlation.py --bootstrap_samples=100 --data_size=2000
Best correlation of 0.726 was found for beta 0.63
python3 metric_correlation.py --bootstrap_samples=200 --data_size=2000
Best correlation of 0.676 was found for beta 0.61
Deadline: Dec 5, 7:59 a.m. 2 points+4 bonus
This assignment is a competition task. Your goal is to submit the smallest model achieving at least 99% accuracy on the MNIST dataset. The total size of your submission must be at most 1 MiB, and the competition points will be awarded according to the size of your submission, which ReCodEx shows in the logs (the accuracy of your solution does not affect the competition points, as long as it is at least 99%, and is therefore not hidden as usual).
The miniaturization.py
template shows how to load training data, downloading it if needed.
Furthermore, it shows how to save a trained estimator and how to load it during
prediction. Finally, it includes a class MLPFullDistributionClassifier
, which
modifies MLPClassifier
to support full categorical distributions on input,
i.e., each label is a distribution over the predicted classes. Such a classifier
might be useful for example for knowledge distillation.
You can use any sklearn/numpy/scipy algorithm to solve this exercise (and of course anything you implement yourself).
Deadline: Dec 12, 7:59 a.m. 4 points
Starting with the decision_tree.py,
manually implement construction of a classification decision tree, supporting both
gini
and entropy
criteria, and max_depth
, min_to_split
and max_leaves
constraints.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 decision_tree.py --dataset=digits --criterion=gini --min_to_split=250
Train accuracy: 60.7%
Test accuracy: 59.6%
python3 decision_tree.py --dataset=digits --criterion=gini --max_depth=3
Train accuracy: 41.1%
Test accuracy: 38.0%
python3 decision_tree.py --dataset=digits --criterion=gini --max_leaves=8
Train accuracy: 60.1%
Test accuracy: 57.1%
python3 decision_tree.py --dataset=digits --criterion=gini --min_to_split=220 --max_leaves=8
Train accuracy: 60.7%
Test accuracy: 59.6%
python3 decision_tree.py --dataset=digits --criterion=entropy --min_to_split=420
Train accuracy: 42.4%
Test accuracy: 40.2%
python3 decision_tree.py --dataset=breast_cancer --criterion=entropy --max_depth=3 --seed=44
Train accuracy: 94.8%
Test accuracy: 93.7%
python3 decision_tree.py --dataset=digits --criterion=entropy --max_leaves=7
Train accuracy: 53.2%
Test accuracy: 51.6%
python3 decision_tree.py --dataset=breast_cancer --criterion=entropy --min_to_split=55 --max_depth=3 --seed=44
Train accuracy: 94.4%
Test accuracy: 93.7%
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 decision_tree.py --dataset=iris --max_depth=3
Train accuracy: 95.5%
Test accuracy: 100.0%
python3 decision_tree.py --dataset=wine --criterion=entropy --min_to_split=20 --seed=44
Train accuracy: 97.7%
Test accuracy: 91.1%
python3 decision_tree.py --dataset=breast_cancer --criterion=entropy --max_depth=3 --seed=44
Train accuracy: 94.8%
Test accuracy: 93.7%
Deadline: Dec 12, 7:59 a.m. 3 points
Using the random_forest.py template, train a random forest, which is a collection of decision trees trained with dataset bagging and random feature subsampling.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 random_forest.py --dataset=digits --trees=10 --max_depth=3
Train accuracy: 56.6%
Test accuracy: 55.8%
python3 random_forest.py --dataset=digits --trees=10 --bagging --max_depth=3
Train accuracy: 71.5%
Test accuracy: 71.8%
python3 random_forest.py --dataset=digits --trees=10 --feature_subsampling=0.5 --max_depth=3
Train accuracy: 69.0%
Test accuracy: 67.6%
python3 random_forest.py --dataset=digits --trees=10 --bagging --feature_subsampling=0.5 --max_depth=3
Train accuracy: 76.6%
Test accuracy: 76.9%
python3 random_forest.py --dataset=wine --trees=10 --max_depth=3
Train accuracy: 98.5%
Test accuracy: 91.1%
python3 random_forest.py --dataset=wine --trees=10 --bagging --max_depth=3
Train accuracy: 98.5%
Test accuracy: 93.3%
python3 random_forest.py --dataset=breast_cancer --trees=10 --feature_subsampling=0.5 --max_depth=3
Train accuracy: 98.6%
Test accuracy: 97.9%
python3 random_forest.py --dataset=breast_cancer --trees=10 --bagging --feature_subsampling=0.5 --max_depth=3
Train accuracy: 96.9%
Test accuracy: 96.5%
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 random_forest.py --dataset=wine --trees=3 --max_depth=3
Train accuracy: 98.5%
Test accuracy: 91.1%
python3 random_forest.py --dataset=wine --trees=3 --bagging --max_depth=3
Train accuracy: 97.0%
Test accuracy: 93.3%
python3 random_forest.py --dataset=wine --trees=3 --feature_subsampling=0.5 --max_depth=3
Train accuracy: 100.0%
Test accuracy: 97.8%
python3 random_forest.py --dataset=wine --trees=3 --bagging --feature_subsampling=0.5 --max_depth=3
Train accuracy: 98.5%
Test accuracy: 91.1%
Deadline: Dec 12, 7:59 a.m. 2 points+4 bonus
The goal of this competition task
is to perform human activity recognition, namely to recognize one
of five actions (walking
, standing
, sitting
, standing up
, sitting down
)
using data from four accelerometers. The train set consists of 50k
examples, the test set of approximately 115k.
The human_activity_recognition.py template shows how to load the training data, downloading it if needed.
Your model will be evaluated using accuracy and your goal is to achieve at least 99.25%. Note that you can use any sklearn algorithm to solve this assignment.
Deadline: Dec 19, 7:59 a.m. 6 points
Using the gradient_boosting.py template, train gradient boosted decision tree forest for classification.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 gradient_boosting.py --dataset=wine --trees=3 --max_depth=1 --learning_rate=0.3
Using 1 trees, train accuracy: 95.5%, test accuracy: 91.1%
Using 2 trees, train accuracy: 95.5%, test accuracy: 86.7%
Using 3 trees, train accuracy: 97.7%, test accuracy: 91.1%
python3 gradient_boosting.py --dataset=wine --trees=3 --max_depth=2 --learning_rate=0.3 --seed=883
Using 1 trees, train accuracy: 100.0%, test accuracy: 88.9%
Using 2 trees, train accuracy: 99.2%, test accuracy: 93.3%
Using 3 trees, train accuracy: 99.2%, test accuracy: 97.8%
python3 gradient_boosting.py --dataset=wine --trees=3 --max_depth=2 --l2=0.5 --learning_rate=0.3 --seed=488
Using 1 trees, train accuracy: 97.0%, test accuracy: 95.6%
Using 2 trees, train accuracy: 98.5%, test accuracy: 97.8%
Using 3 trees, train accuracy: 99.2%, test accuracy: 97.8%
python3 gradient_boosting.py --dataset=digits --trees=3 --max_depth=2 --learning_rate=0.5
Using 1 trees, train accuracy: 79.1%, test accuracy: 76.9%
Using 2 trees, train accuracy: 85.7%, test accuracy: 84.4%
Using 3 trees, train accuracy: 91.3%, test accuracy: 87.8%
python3 gradient_boosting.py --dataset=breast_cancer --trees=3 --max_depth=2 --learning_rate=0.5 --seed=45
Using 1 trees, train accuracy: 94.6%, test accuracy: 90.2%
Using 2 trees, train accuracy: 96.9%, test accuracy: 95.1%
Using 3 trees, train accuracy: 96.9%, test accuracy: 93.7%
Deadline: Dec 19, 7:59 4 points+5 bonus
In this competition task you will be solving the Native Language Identification. In that task, you get an English essay writen by a non-native individual and your goal is to identify their native language.
We will be using NLI Shared Task 2013 data, which contains documents in 11 languages. For each language, the train and test sets contain 1000 and 100 documents, respectively. Particularly interesting is the fact that humans are quite bad in this task (in a simplified settings, human professionals achieve 40-50% accuracy), while machine learning models can achieve high performance.
Because the data is not publicly available, you can download it only through ReCodEx. Please do not distribute it. The template nli_competition.py can then be used to load the dataset as usual.
The performance of your system is measured using accuracy of correctly predicted documents and your goal is to achieve at least 78% accuracy. Note that you can use any sklearn algorithm to solve this exercise.
Deadline: Jan 9, 7:59 a.m. 3 points
Using the pca.py template, implement the PCA computation with both
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 pca.py --max_iter=5
Test set accuracy: 90.48%
python3 pca.py --max_iter=5 --pca=1
Test set accuracy: 30.28%
python3 pca.py --max_iter=5 --pca=5
Test set accuracy: 68.88%
python3 pca.py --max_iter=5 --pca=10
Test set accuracy: 80.00%
python3 pca.py --max_iter=5 --pca=20
Test set accuracy: 87.68%
python3 pca.py --max_iter=5 --pca=50
Test set accuracy: 90.28%
python3 pca.py --max_iter=5 --pca=100
Test set accuracy: 90.76%
python3 pca.py --max_iter=5 --pca=200
Test set accuracy: 90.68%
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 pca.py --max_iter=1000 --solver=lbfgs
Test set accuracy: 89.76%
python3 pca.py --max_iter=1000 --pca=1 --solver=lbfgs
Test set accuracy: 30.88%
python3 pca.py --max_iter=1000 --pca=5 --solver=lbfgs
Test set accuracy: 68.96%
python3 pca.py --max_iter=1000 --pca=10 --solver=lbfgs
Test set accuracy: 80.48%
python3 pca.py --max_iter=1000 --pca=20 --solver=lbfgs
Test set accuracy: 87.80%
python3 pca.py --max_iter=1000 --pca=50 --solver=lbfgs
Test set accuracy: 90.08%
python3 pca.py --max_iter=1000 --pca=100 --solver=lbfgs
Test set accuracy: 90.16%
python3 pca.py --max_iter=1000 --pca=200 --solver=lbfgs
Test set accuracy: 89.88%
Deadline: Jan 9, 7:59 a.m. 3 points
Using the kmeans.py template, implement the K-Means algorithm with both
kmeans++
initialization.Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 kmeans.py --clusters=5 --examples=150 --iterations=3 --init=random
Cluster assignments:
[4 3 4 4 3 2 3 3 4 3 4 4 4 3 1 4 3 4 1 2 3 4 3 3 1 3 3 3 3 4 0 3 3 3 4 0 3
3 4 3 4 3 3 4 4 4 3 0 4 4 4 4 2 3 3 2 4 0 0 3 3 4 4 0 3 2 4 1 3 4 1 4 4 0
4 3 4 1 3 0 3 4 4 3 4 1 3 4 3 3 4 4 4 3 4 4 1 4 4 3 1 3 1 4 3 3 3 4 4 4 4
3 4 4 4 4 4 4 2 4 3 4 4 2 3 3 3 4 2 4 4 3 3 2 3 3 2 3 2 0 4 3 3 3 3 3 3 3
3 4]
python3 kmeans.py --clusters=5 --examples=150 --iterations=3 --init=kmeans++
Cluster assignments:
[4 1 4 2 3 0 1 1 4 3 4 2 2 3 0 2 3 2 0 0 1 4 3 3 0 3 3 1 3 2 0 1 3 3 2 0 3
3 4 1 2 3 1 2 4 2 1 0 2 4 2 2 0 1 1 0 2 0 0 1 3 4 2 0 3 0 4 0 3 2 0 4 2 0
2 1 2 0 3 0 1 2 4 1 4 0 1 2 3 3 2 4 2 1 4 2 0 4 4 1 0 3 0 2 1 3 1 4 2 2 4
3 2 2 4 4 2 4 0 2 1 4 4 0 3 1 3 4 0 2 4 1 1 0 3 1 0 3 0 0 4 1 1 3 3 1 1 3
1 2]
python3 kmeans.py --clusters=7 --examples=200 --iterations=3 --init=random
Cluster assignments:
[6 0 0 3 3 1 1 1 5 4 3 6 5 2 4 4 3 3 4 3 6 3 1 3 0 6 2 0 2 6 2 1 0 3 6 3 1
3 5 3 3 0 4 3 5 6 3 0 3 3 6 6 3 3 3 5 0 5 3 6 2 2 0 2 2 4 3 3 6 6 6 5 0 2
1 2 2 6 2 5 0 0 1 2 5 2 2 4 5 3 6 4 6 3 3 5 1 3 3 0 5 3 0 6 2 0 2 3 5 0 3
1 3 5 6 5 4 2 0 3 2 3 3 6 1 2 2 2 4 3 0 5 5 2 3 5 1 2 6 6 0 5 5 3 3 3 2 6
0 0 2 0 6 3 3 2 6 0 3 5 4 1 3 5 0 3 0 5 6 3 1 5 0 0 3 0 1 3 5 4 3 3 3 2 3
3 2 3 6 1 4 3 0 5 5 3 3 5 6 6]
python3 kmeans.py --clusters=7 --examples=200 --iterations=3 --init=kmeans++
Cluster assignments:
[4 0 0 5 5 2 2 2 2 2 5 4 2 3 2 2 6 6 2 6 4 5 2 1 0 4 3 0 3 4 3 2 0 1 4 6 2
5 2 5 5 0 2 1 2 4 5 0 1 5 4 4 5 5 5 2 0 2 6 4 3 3 0 3 3 2 5 1 4 4 4 2 0 3
2 3 3 4 3 2 0 0 2 3 2 3 3 2 2 5 4 2 4 5 1 2 2 5 1 0 2 1 0 4 3 0 3 1 2 0 6
2 1 2 4 2 2 3 0 5 3 1 1 4 2 3 3 3 2 6 0 2 2 3 1 2 2 3 4 4 0 2 2 1 5 1 3 4
0 0 3 0 4 1 5 3 4 0 1 2 2 2 6 2 0 1 0 2 4 1 2 2 0 0 5 0 2 1 2 2 1 5 1 3 1
5 3 5 4 2 2 6 0 2 2 1 6 2 4 4]
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 kmeans.py --clusters=5 --examples=150 --iterations=5 --seed=51 --init=random
Cluster assignments:
[2 3 3 4 1 2 1 1 2 3 2 1 1 3 3 4 0 4 4 1 3 1 1 1 1 0 1 3 3 2 3 0 1 0 3 3 0
0 1 0 1 2 1 1 3 2 1 2 2 1 3 2 2 2 3 2 1 2 1 4 3 3 4 4 2 1 1 1 1 3 1 3 1 4
1 3 2 1 0 0 1 2 2 0 2 2 3 1 1 1 2 2 4 2 2 1 1 1 2 2 2 3 1 3 1 3 2 1 0 2 2
3 1 1 1 3 3 0 1 3 4 1 1 4 1 3 1 4 4 3 1 4 1 4 1 1 1 3 1 1 4 2 0 3 1 4 1 2
2 1]
python3 kmeans.py --clusters=5 --examples=150 --iterations=5 --seed=51 --init=kmeans++
Cluster assignments:
[1 3 3 4 0 1 0 2 1 3 1 2 2 3 3 4 4 4 4 2 3 2 2 2 2 4 2 3 3 0 3 4 0 4 3 3 4
4 2 4 2 1 0 0 3 1 0 1 1 0 3 1 0 0 3 1 0 1 2 4 3 3 4 4 1 0 2 0 0 3 0 3 0 4
2 3 1 2 4 4 2 1 1 4 1 1 3 0 2 2 1 1 4 1 1 2 0 2 1 1 1 3 0 3 2 3 1 0 4 1 1
3 0 2 0 3 0 4 0 3 4 0 2 4 2 3 2 4 4 3 2 4 2 4 2 0 0 3 0 0 4 1 4 3 0 4 2 1
1 2]
python3 kmeans.py --clusters=7 --examples=200 --iterations=11 --seed=67 --init=random
Cluster assignments:
[2 1 0 4 5 1 4 1 1 2 0 3 6 6 1 6 1 1 0 2 3 2 4 0 6 5 5 4 5 4 4 6 6 1 0 6 4
4 1 6 4 5 4 4 1 0 2 1 2 2 4 3 2 1 5 2 6 0 5 6 4 2 6 3 1 1 4 5 1 2 4 5 4 5
1 1 4 2 5 4 4 5 4 2 2 4 4 1 5 0 4 4 4 1 3 0 3 5 4 1 0 4 4 4 4 4 5 4 1 4 4
2 5 2 6 5 2 2 4 5 4 4 3 3 2 6 1 4 1 6 1 2 3 0 5 6 4 6 4 5 5 2 0 1 6 0 1 4
4 6 5 1 2 4 0 0 4 0 4 3 5 4 3 4 6 3 6 5 5 6 0 2 6 5 4 5 4 3 2 4 1 2 4 2 4
2 6 4 4 6 2 4 4 6 6 5 0 2 4 1]
python3 kmeans.py --clusters=7 --examples=200 --iterations=5 --seed=67 --init=kmeans++
Cluster assignments:
[3 1 4 5 0 1 6 1 3 3 4 4 2 2 1 2 1 1 4 3 4 3 5 4 2 0 0 6 0 6 5 2 2 1 4 2 5
5 1 2 5 0 6 6 1 4 3 1 3 3 5 4 3 1 0 3 2 4 0 2 5 3 2 4 1 1 6 0 1 3 5 0 5 0
1 1 6 3 0 6 5 0 5 3 3 5 6 1 0 4 5 6 5 1 4 4 2 0 6 1 4 6 5 5 6 5 0 5 1 6 6
3 0 3 2 0 3 3 5 0 6 6 4 4 3 2 1 6 1 2 1 3 4 4 0 2 6 2 6 0 0 3 4 1 2 4 1 5
6 2 0 1 3 5 4 4 6 4 6 4 0 5 2 5 2 4 2 0 0 2 4 3 2 0 6 0 5 2 3 5 1 3 6 3 5
3 2 6 5 2 0 6 6 2 2 0 4 3 6 1]
Deadline: Feb 18, 23:59 3 points
Given two trained models, compute their 95% confidence intervals using bootstrap resampling. Then, estimate the probability that the second one is better than the first one using a paired bootstrap test.
Start with the bootstrap_resampling.py template. Note that you usually need to perform a lot of the bootstrap resamplings, so you should make sure your implementation is fast enough.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 bootstrap_resampling.py --seed=47 --test_size=0.9 --bootstrap_samples=1000
Confidence intervals of the two models:
- [91.41% .. 93.88%]
- [91.96% .. 94.38%]
The estimated probability that the null hypothesis holds: 2.10%
python3 bootstrap_resampling.py --seed=47 --test_size=0.9 --bootstrap_samples=10000
Confidence intervals of the two models:
- [91.35% .. 93.88%]
- [91.97% .. 94.38%]
The estimated probability that the null hypothesis holds: 2.40%
python3 bootstrap_resampling.py --seed=47 --test_size=0.9 --bootstrap_samples=100000
Confidence intervals of the two models:
- [91.35% .. 93.88%]
- [91.97% .. 94.38%]
The estimated probability that the null hypothesis holds: 2.21%
python3 bootstrap_resampling.py --seed=55 --test_size=0.95 --bootstrap_samples=50000
Confidence intervals of the two models:
- [84.72% .. 88.00%]
- [85.30% .. 88.52%]
The estimated probability that the null hypothesis holds: 5.47%
Deadline: Feb 18, 23:59 2 point
Given two trained models, perform a random permutation test that the second one is better than the first one.
Start with the permutation_test.py template. Note that you usually need to perform a lot of resamplings, so you should make sure your implementation is fast enough.
Note that your results may be slightly different (because of varying floating point arithmetic on your CPU).
python3 permutation_test.py --seed=47 --test_size=0.9 --random_samples=1000
The estimated p-value of the random permutation test: 3.10%
python3 permutation_test.py --seed=47 --test_size=0.9 --random_samples=10000
The estimated p-value of the random permutation test: 2.99%
python3 permutation_test.py --seed=47 --test_size=0.9 --random_samples=100000
The estimated p-value of the random permutation test: 3.11%
python3 permutation_test.py --seed=55 --test_size=0.95 --random_samples=50000
The estimated p-value of the random permutation test: 6.55%
In the competitions, your goal is to train a model and then predict target values on the test set available only in ReCodEx.
When submitting a competition solution to ReCodEx, you should submit a trained model and a Python source capable of running it.
Furthermore, please also include the Python source and hyperparameters
you used to train the submitted model. But be careful that there still must be
exactly one Python source with a line starting with def main(
.
Do not forget about the maximum allowed model size and time and memory limits.
Before the deadline, ReCodEx prints the exact achieved score, but only if it is worse than the baseline.
If you surpass the baseline, the assignment is marked as solved in ReCodEx and you immediately get regular points for the assignment. However, ReCodEx does not print the reached score.
After the competition deadline, the latest submission of every user surpassing the required baseline participates in a competition. Additional bonus points are then awarded according to the ordering of the performance of the participating submissions.
After the competition results announcement, ReCodEx starts to show the exact performance for all the already submitted solutions and also for the solutions submitted later.
Each competition will be scored after the first deadline.
The bonus points will be computed in the following fashion:
Let $B$ be the maximal number of bonus points that can be achieved in the competition;
All of the solutions that surpass the baseline will be sorted and divided into $B+1$ groups of equal size;
Every solution in the top group gets B points, the next group gets $B-1$ points, etc., the last group gets 0 bonus points.
The team solution only occupies one position in the table of the competition results.
Plese, do not forget that every member of the team needs to upload the solution to ReCodEx and to submit both the training/prediction source code and the trained model itself.
numpy
or scipy
, anything you implement yourself, and any pre/post-processing
or ensembling methods in sklearn
. Apart from the allowed algorithms,
the implementation must be created by you and you must understand it fully.
Do not use deep network frameworks like TensorFlow or PyTorch.Installing to central user packages repository
You can install all required packages to central user packages repository using
pip3 install --user scikit-learn==1.3.0 numpy==1.24.2 scipy==1.11.2 pandas==2.1.0 matplotlib==3.7.2
.
Installing to a virtual environment
Python supports virtual environments, which are directories containing
independent sets of installed packages. You can create a virtual environment
by running python3 -m venv VENV_DIR
followed by
VENV_DIR/bin/pip3 install scikit-learn==1.3.0 numpy==1.24.2 scipy==1.11.2 pandas==2.1.0 matplotlib==3.7.2
(or VENV_DIR/Scripts/pip3
on Windows).
Windows installation
python3
is not in PATH, while py
command
is – in that case you can use py -m venv VENV_DIR
, which uses the newest
Python available, or for example py -3.11 -m venv VENV_DIR
, which uses
Python version 3.11.Is it possible to keep the solutions in a Git repository?
Definitely. Keeping the solutions in a branch of your repository, where you merge them with the course repository, is probably a good idea. However, please keep the cloned repository with your solutions private.
On GitHub, do not create a public fork with your solutions
If you keep your solutions in a GitHub repository, please do not create a clone of the repository by using the Fork button – this way, the cloned repository would be public.
Of course, if you just want to create a pull request, GitHub requires a public fork and that is fine – just do not store your solutions in it.
How to clone the course repository?
To clone the course repository, run
git clone https://github.com/ufal/npfl129
This creates the repository in the npfl129
subdirectory; if you want a different
name, add it as a last parameter.
To update the repository, run git pull
inside the repository directory.
How to keep the course repository as a branch in your repository?
If you want to store the course repository just in a local branch of your existing repository, you can run the following command while in it:
git remote add upstream https://github.com/ufal/npfl129
git fetch upstream
git checkout -t upstream/master
This creates a branch master
; if you want a different name, add
-b BRANCH_NAME
to the last command.
In both cases, you can update your checkout by running git pull
while in it.
How to merge the course repository with your modifications?
If you want to store your solutions in a branch merged with the course repository, you should start by
git remote add upstream https://github.com/ufal/npfl129
git pull upstream master
which creates a branch master
; if you want a different name,
change the last argument to master:BRANCH_NAME
.
You can then commit to this branch and push it to your repository.
To merge the current course repository with your branch, run
git merge upstream master
while in your branch. Of course, it might be necessary to resolve conflicts if both you and I modified the same place in the templates.
What files can be submitted to ReCodEx?
You can submit multiple files of any type to ReCodEx. There is a limit of 20 files per submission, with a total size of 20MB.
What file does ReCodEx execute and what arguments does it use?
Exactly one file with py
suffix must contain a line starting with def main(
.
Such a file is imported by ReCodEx and the main
method is executed
(during the import, __name__ == "__recodex__"
).
The file must also export an argument parser called parser
. ReCodEx uses its
arguments and default values, but it overwrites some of the arguments
depending on the test being executed – the template should always indicate which
arguments are set by ReCodEx and which are left intact.
What are the time and memory limits?
The memory limit during evaluation is 1.5GB. The time limit varies, but it should be at least 10 seconds and at least twice the running time of my solution. For competition assignments, the time limit is 5 minutes.
To pass the practicals, you need to obtain at least 70 points, excluding the bonus points. Note that up to 40 points above 70 (both bonus and non-bonus) will be transfered to the exam. In total, assignments for at least 105 points (not including the bonus points) will be available.
To pass the exam, you need to obtain at least 60, 75, or 90 points out of 100-point exam to receive a grade 3, 2, or 1, respectively. The exam consists of 100-point-worth questions from the list below (the questions are randomly generated, but in such a way that there is at least one question from every pair of lectures). In addition, you can get at most 40 surplus points from the practicals and at most 10 points for community work (i.e., fixing slides or reporting issues) – but only the points you already have at the time of the exam count. You can take the exam without passing the practicals first.
Lecture 1 Questions
Explain why we need separate train and test data? What is generalization and how the concept relates to underfitting and overfitting? [10]
Define prediction function of a linear regression model and write down $L^2$-regularized mean squared error loss. [10]
Starting from unregularized sum of squares error of a linear regression model, show how the explicit solution can be obtained, assuming $\boldsymbol X^T \boldsymbol X$ is regular. [20]
Lecture 2 Questions
Describe standard gradient descent and compare it to stochastic (i.e., online) gradient descent and minibatch stochastic gradient descent. [10]
Write an $L^2$-regularized minibatch SGD algorithm for training a
linear regression model, including the explicit formulas (i.e,. formulas you would
need to code it in numpy
) of the loss function and its gradient. [10]
Does the SGD algorithm for linear regression always find the best solution on the training data? If yes, explain under what conditions it happens, if not explain why it is not guaranteed to converge. [20]
After training a model with SGD, you ended up with a low training error and a high test error. Using the learning curves, explain what might have happened and what steps you might take to prevent this from happening. [10]
You were provided with a fixed training set and a fixed test set and you are supposed to report model performance on that test set. You need to decide what hyperparameters to use. How will you proceed and why? [5]
What method can be used for normalizing feature values? Explain why it is useful. [5]
Lecture 3 Questions
Define binary classification, write down the perceptron algorithm and show how a prediction is made for a given example. [10]
For discrete random variables, define entropy, cross-entropy, Kullback-Leibler divergence, and prove the Gibbs inequality (i.e., that KL divergence is non-negative). [20]
Explain the notion of likelihood in maximum likelihood estimation. [5]
Describe maximum likelihood estimation as minimizing NLL, cross-entropy, and KL divergence and explain the relation between them. [20]
Considering binary logistic regression model, write down its parameters (including their size) and explain how prediction is performed (including the formula for the sigmoid function). [10]
Write down an $L^2$-regularized minibatch SGD algorithm for training a binary
logistic regression model, including the explicit formulas (i.e., formulas
you would to code it in numpy
) of the loss function and its gradient. [20]
Lecture 4 Questions
Define mean squared error and show how it can be derived using MLE. [10]
Considering $K$-class logistic regression model, write down its parameters (including their size) and explain how prediction is performed (including the formula for the softmax function). [10]
Explain the relationship between the sigmoid function and softmax. [5]
Write down an $L^2$-regularized minibatch SGD algorithm for training a
$K$-class logistic regression model, including the explicit formulas (i.e.,
formulas you would to code it in numpy
) of the loss function and its
gradient. [20]
Prove that decision regions of a multiclass logistic regression are convex. [10]
Considering a single-layer MLP with $D$ input neurons, $H$ hidden neurons, $K$ output neurons, hidden activation $f$, and output activation $a$, list its parameters (including their size) and write down how the output is computed. [10]
List the definitions of frequently used MLP output layer activations (the ones producing parameters of a Bernoulli distribution and a categorical distribution). Then write down three commonly used hidden layer activations (sigmoid, tanh, ReLU). [10]
Lecture 5 Questions
Considering a single-layer MLP with $D$ input neurons, a ReLU hidden layer
with $H$ units and a softmax output layer with $K$ units, write down the
explicit formulas (i.e., formulas you would to code it in numpy
) of the
gradient of all the MLP parameters (two weight matrices and two bias
vectors), assuming input $\boldsymbol x$, target $t$, and negative log
likelihood loss. [20]
Formulate the Universal approximation theorem. [10]
How do we search for a minimum of a function $f(\boldsymbol x): \mathbb{R}^D \rightarrow \mathbb{R}$ subject to equality constraints $g_1(\boldsymbol x)=0, \ldots, g_m(\boldsymbol x)=0$? [10]
Prove which categorical distribution with $N$ classes has maximum entropy. [10]
Consider derivation of softmax using maximum entropy principle, assuming we have a dataset of $N$ examples $(x_i, t_i), x_i \in \mathbb{R}^D, t_i \in \{1, 2, \ldots, K\}$. Formulate the three conditions we impose on the searched $\pi: \mathbb{R}^D \rightarrow \mathbb{R}^K$, and write down the Lagrangian to be minimized. [20]
Define precision (including true positives and others), recall, $F_1$ score, and $F_\beta$ score (we stated several formulations for $F_1$ and $F_\beta$ scores; any one of them will do). [10]
Explain the difference between micro-averaged and macro-averaged $F_1$ scores. [10]
Explain (using examples) why accuracy is not a suitable metric for unbalanced target classes, e.g., for a diagnostic test for a contagious disease. [5]
Lecture 6 Questions
Explain how is the TF-IDF weight of a given document-term pair computed. [5]
Define conditional entropy, mutual information, write down the relation between them, and finally prove that mutual information is zero if and only if the two random variables are independent (you do not need to prove statements about $D_\textrm{KL}$). [10]
Show that TF-IDF terms can be considered portions of suitable mutual information. [10]
Explain the concept of word embedding in the context of MLP and how it relates to representation learning. [5]
Describe the skip-gram model trained using negative sampling. [10]
How would you proceed to train a part-of-speech tagger (i.e., you want to assign each word with its part of speech) if you only could use pre-trained word embeddings and MLP classifier? [5]
Lecture 7 Questions
Describe $k$-nearest neighbors prediction, both for regression and classification. Define $L_p$ norm and describe uniform, inverse, and softmax weighting. [10]
Show that $L^2$-regularization can be obtained from a suitable prior by Bayesian inference (from the MAP estimate). [10]
Write down how $p(C_k | \boldsymbol x)$ is approximated in a Naive Bayes classifier, explicitly state the Naive Bayes assumption, and show how is the prediction performed. [10]
Considering a Gaussian naive Bayes, describe how are $p(x_d | C_k)$ modeled (what distribution and which parameters does it have) and how we estimate it during fitting. [10]
Considering a Bernoulli naive Bayes, describe how are $p(x_d | C_k)$ modeled (what distribution and which parameters does it have) and how we estimate it during fitting. [10]
Lecture 8 Questions
Prove that independent discrete random variables are uncorrelated. [10]
Write down the definition of covariance and Pearson correlation coefficient $\rho$, including its range. [10]
Explain how are the Spearman's rank correlation coefficient and the Kendall rank correlation coefficient computed (no need to describe the Pearson correlation coefficient). [10]
Describe setups where a correlation coefficient might be a good evaluation metric. [5]
Describe under what circumstance correlation can be used to assess validity of evaluation metrics. [5]
Define Cohen's $\kappa$ and explain what it is used for when preparing data for machine learning. [10]
Considering an averaging ensemble of $M$ models, prove the relation between the average mean squared error of the ensemble and the average error of the individual models, assuming the model errors have zero means and are uncorrelated. [20]
Explain knowledge distillation: what it is used for, describe how it is done. [10]
Lecture 9 Questions
In a regression decision tree, state what values are kept in internal nodes, define the squared error criterion and describe how is a leaf split during training (without discussing splitting constraints). [10]
In a $K$-class classification decision tree, state what values are kept in internal nodes, define the Gini index and describe how is a node split during training (without discussing splitting constraints). [10]
In a $K$-class classification decision tree, state what values are kept in internal nodes, define the entropy criterion and describe how is a node split during training (without discussing splitting constraints). [10]
For binary classification, derive the Gini index from a squared error loss. [20]
For $K$-class classification, derive the entropy criterion from a non-averaged NLL loss. [20]
Describe how is a random forest trained (including bagging and a random subset of features) and how is prediction performed for regression and classification. [10]
Lecture 10 Questions
Write down the loss function which we optimize in gradient boosted decision trees during the construction of $t^\mathrm{th}$ tree. Then define $g_i$ and $h_i$ and show the value $w_\mathcal{T}$ of optimal prediction in node $\mathcal{T}$ and the criterion used during node splitting. [20]
For a $K$-class classification, describe how to perform prediction with a gradient boosted decision tree trained for $T$ time steps (how the individual trees perform prediction and how are the $K \cdot T$ trees combined to produce the predicted categorical distribution). [10]
What type of data are gradient boosted decision trees good for as opposed to multilayer perceptron? Explain the intuition why it is the case. [5]
Lecture 11 Questions
Formulate SVD decomposition of matrix $\boldsymbol X$, describe properties of individual parts of the decomposition. Explain what the reduced version of SVD is. [10]
Formulate the Eckart-Young theorem. [10]
Explain how to compute the PCA of dimension $M$ using the SVD decomposition of a data matrix $\boldsymbol X$, and why it works. [10]
Given a data matrix $\boldsymbol X$, write down the algorithm for computing the PCA of dimension $M$ using the power iteration algorithm. [20]
Describe the K-means algorithm, including the kmeans++
initialization. [20]
Lecture 12 Questions
Considering statistical hypothesis testing, define type I errors and type II errors (in terms of the null hypothesis). Finally, define what a significance level is. [10]
Explain what a test statistic and a p-value are. [10]
Write down the steps of a statistical hypothesis test, including a definition of a p-value. [10]
Explain the differences between a one-sample test, two-sample test, and a paired test. [10]
When considering multiple comparison problem, define the family-wise error rate, and prove the Bonferroni correction, which allows limiting the family-wise error rate by a given $\alpha$. [10]
For a trained model and a given test set with $N$ examples and metric $E$, write how to estimate 95% confidence intervals using bootstrap resampling. [10]
For two trained models and a given test set with $N$ examples and metric $E$, explain how to perform a paired bootstrap test that the first model is better than the other. [10]
For two trained models and a given test set with $N$ examples and metric $E$, explain how to perform a random permutation test that the first model is better than the other with a significance level $\alpha$. [10]
Lecture 13 Questions
Explain the difference between deontological and utilitarian ethics. List examples on how these theoretical frameworks can be applied in machine learning ethics. [10]
List a few examples of potential ethical problems related to data collection. [5]
List a few examples of potential ethical problems that can originate in model evaluation. [5]