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): HW2, HW2-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