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Lecture

Date Topic Reading Due
9/1 T Intro to ML and probability review [slides] Bishop, Ch 1 (up to 1.2.3) and Ch 2.1-2.2 Self-assessment test by Dr. Aarti Singh
9/3 R Probability estimation [slides] Bishop, Ch 1 (up to 1.2.3) and Ch 2.1-2.2
9/8 T Naive Bayes classifier [slides]
9/10 R Gaussian Naive Bayes classifier [slides] [note] HW1 out
9/15 T Logistic regression [slides]
9/17 R Perceptron [slides]
9/22 T Linear regression [slides]
  • Hastie/Tibshirani/Friedman Ch. 3-Ch 3.2.1, Ch 3.2.4, Ch 3.4.1 (up to page 64)
HW 1 due
9/24 R Neural network [slides]
  • Mitchell Ch 4, Bishop Ch 5
9/29 T k-NN, decision tree [slides]
  • Mitchell Ch 3, Bishop Ch 14.4
10/1 R K-means, hierarchical clustering [slides]
  • Hastie et al. Ch 14.3.12, Bishop Ch 9.1
HW2 due
10/6 T Mixture model [slides]
  • Bishop Ch 9.2-9.4
10/8 R Dimensionality reduction [slides]
  • Bishop Ch 12.1
10/13 T Semi-supervised learning [slides] HW3 due
10/15 R Graphical model 1: model and representation [slides]
  • Bishop Ch 8.1-8.2
10/20 T Graphical model 2: inference [slides]
  • Koller & Friedman Ch 9.2 (copy available on Piazza)
10/22 R Graphical model 3: learning [slides] Project proposal due
10/27 T Hidden Markov models: model and inference [slides] HW4 due
10/29 R Hidden Markov models: inference
11/3 T Hidden Markov models: EM algortihm for learning II [slides]
11/5 R Model selection, regularization [slides] [notes]
  • Bishop Ch 3.1.4, Ch 14.1
  • Hastie et al. Ch 3.3-3.4
11/10 T Support vector machine 1 [slides]
  • Bishop Ch 7.1
HW5 due
11/12 R Support vector machine 2 [slides]
  • Bishop Ch 7.1, Appendix E, Ch 6.1-6.2
11/17 T Midterm review [slides]
11/19 R Midterm
11/24 T Boosting [slides]
  • Bishop Ch 14.3
12/1 T Learning theory [slides]
  • Mitchell Ch 7.1-7.4
12/3 R Learning theory, overfitting, bias-variance trade-off [slides]
  • Bishop Ch 3.2
Project mid-report due
12/8 T Markov decision process and reinforcement learning [slides]
  • Mitchell Ch 13
  • See the book chapter on MDP on Piazza
12/10 R Active learning [slides] HW6 due