Introduction to Machine Learning and Statistical Inference 
MICAS911-SI221 (2021) 

14/09:  Why ML? ML and the broader landscape, ML vs. AI (UML Chapters 1,2): info, notations, HW1, HW1-sol

21/09:  PAC/APAC model of learning, supervised and unsupervised learning as special cases, ERM, No Free Lunch Theorem (UML Chapter 3): HW2HW2-sol

28/09:  Learning through uniform convergence, shattering, VC dimension (UML Chapters 4,5,6): HW3

5/10:  What can/cannot be learned, statistical vs. computational complexity of learning (UML Chapters 6,7,8 lightly): HW3 (cont), HW3-sol    

12/10: Linear separators (UML Chapters 9, 9.1): HW4

19/10: Linear regression, logistic regression (UML Chapter 9, see here for sample complexity of linear regression): HW5  Pycode  

26/10: Model selection/validation, K-NN, K-Means (UML Chapters 11, 19, 22): HW6

27/10 (MICAS only but everybody is welcome!): Boosting, PCA (UML Chapters 10, 23): HW7, Pycode

29/10 (MICAS only): Revision, end of exercises

9/11: Exam