Jan 5: Introduction and Administrivia: Slides
Jan 7: A Geometric View of Linear Algebra: Notes
Jan 12: – contd. –
Jan 14: Classification: Nearest Neighbors: Notes
Jan 19: – contd. –
Jan 21: Decision Trees Notes
Jan 26: No lecture.
Jan 28: Decision Trees contd.
Feb 2: – contd. –
Feb 4: Midterm
Feb 9: Perceptron Notes
Feb 11: Midterm discussion. Perceptron contd.
Feb 16: Finish Perceptron Notes Begin Kernels Notes
Feb 19: Kernels contd.
Feb 23: – contd. –
Feb 25: Finish Kernels. Begin Bias-Variance Notes
Mar 1: Problem Session.
Mar 3: No lecture.
Mar 8: Boosting Notes
Mar 10: Finish up Boosting. Practical tips on how to do Machine Learning.