Credit points: 3.0
Final grade:
60% final exam (mandatory grade>55)
40% HW (4% for the first HW and 9% for each one HW(2-5) )
The course provides an introduction to the field of machine learning.
In the following, we bring the week by week schedule. Let us note that this is subject to changes (if such required).
L1: Introduction
T1: Data Processing With Pandas
L2: Data Preparation I
T2: Probability, Linear Algebra And Statistics Refresher
L3: Data Preparation Ii
T3: Dimensionality Reduction And Feature Selection
L4: Classification I
T4: Linear Models For Classification
L5: Classification Ii
T5: Decision Trees
L6: Linear Regression
T6: Linear Regression
L7: Validation And Model Selection
T7: Evaluation Of Learning Models
L8: Bayesian Learning And Bayes Networks
T8: Bayesian Learning And Bayes Networks
L9: Unsupervised Learning And Clustering
T9: Expectation Maximization (K-Means And Gmm)
L10: Ensemble Learning And Adaboost
T10: Bagging And Boosting
L11: Deep Learning For Classification And Regression
T11: Intro To Deep Learning
L12: Unsupervised Deep Learning And Autoencoders
T12: Unsupervised Deep Learning
L13: TBD
T13: PAC Learning