Content and tentative timeline

Date	Lecture	Notes
Week 1 Oct 6	Introduction; Supervised Learning Setup; kNNs	Assignment: Project One - KNNs
Week 2 Oct 13	Linear models for regression
Week 3 Oct 20	Linear Models for Classification; Logistic regression; Gradient Descent
Week 4 Oct 25	Linear models
Week 5 Nov 10	The Perceptron;	Assignment: Project Two - The Perceptron
Week 6 Nov 17	Support Vector Machines
Week 7 Nov 24	Gradient Descent
Week 9 Dec 1	Learning Theory
Week 10 Dec 8	Kernels;	Assignment: Project 3
Week 11 Dec 15	Support Vector Machines
Week 12 Jan 12	Trees; Ensembles	Assignment: Project 4
Week 13 Jan 19	Trees & Ensembles
Week 14 Jan 26	Unsupervised learning; Clustering
Week 15 Feb 2	Neural Networks; Revision