NPFL104 Machine Learning Methods

Summer Term 2017/2018

Course aim

To provide students with an intensive practical experience on applying Machine Learning techniques on real data.

Course strategy

Until (time) exhausted, loop as follows:
  1. Do-It-Yourself step - develop your own toy implementations of ML basic techniques (in Python), to understand the core concepts,
  2. Do-It-Well step - learn to use existing Python libraries and routinize their application on a number of example datasets

Course schedule

Week 1 - Introduction

Week 2 - Selected classification methods

Week 3 - Selected classification methods, cont.

Week 4 - Regression

Week 5 - ML Diagnostics

Week 6 - Feature engineering, Regularization

Week 7 - Kernel methods

Week 8 - Clustering

Week 9 - Clustering, cont.


Required work

Final exam test

Determination of final grade