Quick Links: Office Hours Piazza Textbook Schedule

Instructor:Rishit ShethOffice Hours: Thurs 67pm Location for Office Hours: Halligan 235B Email: rishit.sheth@tufts.edu 

Teaching Assistant:Xinmeng LiOffice Hours: Mon 78p, Tues 12p, Wed 78p, and Fri 121p Location for Office Hours: Halligan Extension Email: xinmeng.li@tufts.edu 

Tufts and the teaching staff of COMP 136 strive to create a learning environment that is welcoming to students of all backgrounds. If you feel unwelcome for any reason, please let us know so we can work to make things better. You can let us know by talking to anyone on the teaching staff. If you feel uncomfortable talking to members of the teaching staff, consider reaching out to your academic advisor, the department chair, or your dean.
Date  Topics  Lecture  Due 
Thurs, Jan 17  Probabilistic models  Introduction to course. A simple probabilistic model.  Skim read Chapter 1. 
Tues, Jan 22  Probabilistic models  Probability distributions. Parameter estimation. Written assignment 1 out.  Read Sections 1.2.4, 2.1, 2.2. 
Thurs, Jan 24  Linear regression  Least squares.  Read Section 3.1. 
Tues, Jan 29  Linear regression  Linear algebra review part 1. Programming project 1 (data) out.  Skim Appendix C. Written assignment 1 due. 
Thurs, Jan 31  Linear regression  Linear algebra review part 2.  Read Section 2.3. 
Tues, Feb 5  Linear regression  Linear algebra review part 3. Multivariate normal (Gaussian templates). Bayesian linear regression.  Read Section 3.3. 
Thurs, Feb 7  Model selection  Inclass quiz. Written assignment 2 out.  Read Sections 1.3, 3.43.5. Programming project 1 due. 
Tues, Feb 12  Review  
Thurs, Feb 14  Classification  Discriminants. Generative models part 1.  Read Chapter 4 up through Section 4.2. 
Tues, Feb 19  Classification (and probabilistic models)  Inclass quiz ("redo" of Feb 7 quiz). Generative models part 2 (and the exponential family). Programming project 2 (data) out.  (The exponential family is covered in Section 2.4.) Written assignment 2 due. 
Thurs, Feb 21  No class  
Tues, Feb 26  Classification  Discriminative models and logistic regression.  Read Section 4.3. 
Thurs, Feb 28  Classification  Inclass quiz. Bayesian logistic regression. Written assignment 3 out.  Read Sections 4.44.5. 
Tues, Mar 5  Kernels  Gaussian processes.  Read Sections 6.4.16.4.6. Programming project 2 due. 
Thurs, Mar 7  Kernels  Dual representation. Constructing kernels.  Read Chapter 6 up through Section 6.2. 
Tues, Mar 12  Kernels  Support vector machines part 1. Programming project 3 (data) out.  Read Chapter 7 up througth 7.1. Written assignment 3 due. 
Thurs, Mar 14  Kernels  Support vector machines part 2.  
Tues, Mar 19  No class  
Thurs, Mar 21  No class  
Tues, Mar 26  Graphical models  Bayesian networks. Conditional independence.  Read Chapter 8 up througth 8.2. 
Thurs, Mar 28  Graphical models  Inclass quiz. Markov random fields. Inference part 1. Written assignment 4 out.  Read Sections 8.38.4.3. Programming project 3 due. 
Tues, Apr 2  Graphical models  Inference part 2.  Read Sections 8.4.48.4.7. Topic proposal due. 
Thurs, Apr 4  Sampling  Basic sampling. Markov chain Monte Carlo.  Read Chapter 11 up through 11.1.4 and Sections 11.211.3. 
Tues, Apr 9  Sampling  Latent Dirichlet allocation. Programming project 4 out.  Read the paper. Written assignment 4 due. 
Thurs, Apr 11  Model selection  Expectation maximization part 1.  
Tues, Apr 16  Model selection  Expectation maximization part 2.  
Thurs, Apr 18  Variational inference  Inclass quiz.  Programming project 4 due. 
Tues, Apr 23  Variational inference  
Thurs, Apr 25  Unsupervised learning  Short report due.  
79p, Mon, May 6  Final Exam 