Date

Topic

Reading

Due

9/1 T

Intro to ML and probability review [slides]

Bishop, Ch 1 (up to 1.2.3) and Ch 2.12.2

Selfassessment test by Dr. Aarti Singh

9/3 R

Probability estimation [slides]

Bishop, Ch 1 (up to 1.2.3) and Ch 2.12.2


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. 3Ch 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

kNN, decision tree
[slides]


Mitchell Ch 3, Bishop Ch 14.4


10/1 R

Kmeans, hierarchical clustering
[slides]


Hastie et al. Ch 14.3.12, Bishop Ch 9.1

HW2 due

10/6 T

Mixture model
[slides]



10/8 R

Dimensionality reduction
[slides]



10/13 T

Semisupervised learning
[slides]


HW3 due

10/15 R

Graphical model 1: model and representation
[slides]



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 3.33.4


11/10 T

Support vector machine 1
[slides]


HW5 due

11/12 R

Support vector machine 2
[slides]


Bishop Ch 7.1, Appendix E, Ch 6.16.2


11/17 T

Midterm review
[slides]



11/19 R

Midterm



11/24 T

Boosting
[slides]



12/1 T

Learning theory
[slides]



12/3 R

Learning theory, overfitting, biasvariance tradeoff
[slides]


Project midreport 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
