This schedule is meant as an outline. Depending on progress, material may be added or removed. Also, there will often be interesting tangents to follow.

Date Topics Covered Discussion
1 Jan. 07 - 11     Course Overview & kNN
Cross-validation
Leave-one-out
dis. 1
2 Jan. 14 - 18     Decision Tree & Naive Bayes
Entropy and Gini impurity
Reduced-Error Pruning
Naive Bayes assumption
PA1 due Jan. 25
dis. 2
3 Jan. 21 - 25 Linear Regression
Residual Sum of Squares
Nonlinear basis
Regularization
TA1 due Feb. 01
dis. 3
4 Jan. 28 - Feb. 01 Perceptron & Logistic Regression
Gradient Descent
Surrogate Losses
Multiclass Classification
PA2 due Feb. 08
dis. 4
5 Feb. 04 - 08 Neural Networks
Backpropagation
Preventing overfitting
TA2 due Feb. 15
dis. 5
6 Feb. 11 - 15 Convolutional Neural Networks
PA3 due Mar. 10
dis. 6
7 Feb. 18 - 22 Kernels & Clustering
Mercer Theorem
Kernelizing ML algorithms
K-means clustering
dis. 7
8 Feb. 25 - Mar. 01 Review for exam
Exam - I
no discussions
9 Mar. 04 - 08 Support Vector Machines
Linear Programming
Lagrangian Duality
KKT conditions
Dual SVM
TA3 due Mar. 24
dis. 8
10 Mar. 18 - 22 Boosting & Gaussian Mixture Models
AdaBoost
dis. 9
11 Mar. 25 - 29 Gaussian Mixture Models
EM algorithm
Density estimation
TA4 due Apr. 19
dis. 10
12 Apr. 01 - 05 Hidden Markov Models
Markov chains
Viterbi algorithm
PA4 due Apr. 21
dis. 11
13 Apr. 08 - 12 HMM & PCA
Baum-Welch algorithm
PCA algorithm
dis. 12
14 Apr. 15 - 19 Reinforcement Learning
E.Brunskill notes
Multi-Armed Bandits
Markov Decision Processes
Bellman's optimality principle
no discussions
15 Apr. 22 - 26 Review for exam
Exam - II
no discussions