Lecture date | Lecture | Lab | |
1. | Feb 16 |
Introductory lesson (will be finished next time) |
Annotation experiment – Demo
Working with R – Tutorial on annotation data analysis |
2. | Feb 23 | Data analysis, Clustering |
Programming questions - ml-lab.2022-02.25.R |
3. | Mar 2 |
Decision Trees – basic structure |
Inter-annotator agreement – Cohen's kappa
Tutorial on Decision Trees – simple exercise in R |
4. | Mar 9 | Linear regression |
Programming questions - ml-lab.2022-03-11.R |
5. | Mar 16 | lecture cancelled | lab session cancelled |
6. | Mar 23 | Logistic regression, evaluation of binary classifiers |
Programming questions - ml-lab.2022-03-25.R |
7. | Mar 30 |
Learning Decision Trees & Random Forests – Entropy – Learning algorithms (Random Forests will be explained next time) |
Decision Trees, evaluation, and overfitting Example code + illustrations |
8. | Apr 6 | Support Vector Machines, Naive Bayes classifier | |
9. | Apr 13 |
Test #1 – obligatory
Ensemble learning – bagging and boosting |
No lab session (Easter) |
10. | Apr 20 |
HW #1 due date |
|
11. | Apr 27 |
Statistical tests – applications in ML – general principles – t-test and its use – example code |
Exercise on t-test
Pearson's chi-squared tests |
12. | May 4 | Principa Component Analysis, Maximum Likelihood Estimation |
- ml-lab.PCA.2022-05.06.R - ml-lab.SVM.2022-05.06.R |
13. | May 11 | No lecture (Rector's Day) |
Students' presentations Discussion on HW #2 |
14. | May 18 |
Remarks on Bayes classifier and Bayes error
Fundamentals of Neural Networks – HW #2 due date |
Students' presentations
Discussion on HW #2 |
May 31, 9:00 | Exam, room S1 | ||
June 8, 9:00 | Exam, room S10 | ||
June 15, 9:00 | Exam, room S1 |
This course was originally focused on machine learning in natural language processing. To get credits for lab sessions, students needed to do experimental projects