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). Discriminative models and logistic regression part 1. Programming project 2 (data) out.  (The exponential family is covered in Section 2.4.) Read Section 4.3. Written assignment 2 due. 
Thurs, Feb 21  No class  
Tues, Feb 26  Classification  Logistic regression part 2. Bayesian logistic regression part 1.  Read Sections 4.44.5. 
Thurs, Feb 28  Classification  Inclass quiz. Bayesian logistic regression part 1. Written assignment 3 out.  Programming project 2 due. 
Tues, Mar 5  Kernels  Gaussian processes.  Read Sections 6.4.16.4.6. 
Thurs, Mar 7  Kernels  Dual representation. Constructing kernels.  Read Chapter 6 up through Section 6.2. 
Tues, Mar 12  Kernels  Support vector machines. Programming project 3 out.  Read Chapter 7 up througth 7.1. Written assignment 3 due. 
Thurs, Mar 14  Graphical models  
Thurs, Mar 19  No class  
Thurs, Mar 21  No class  
Tues, Mar 26  Graphical models  
Thurs, Mar 28  Graphical models  Inclass quiz. Written assignment 4 out.  Programming project 3 due. 
Tues, Apr 2  Model selection, cont'd  Expectation maximization.  Topic proposal due. 
Thurs, Apr 4  Model selection, cont'd  
Tues, Apr 9  Graphical models, cont'd  Programming project 4 out.  Written assignment 4 due. 
Thurs, Apr 11  Graphical models, cont'd  
Tues, Apr 16  TBA  
Thurs, Apr 18  TBA  Inclass quiz.  Programming project 4 due. 
Tues, Apr 23  TBA  
Thurs, Apr 25  TBA  Short report due.  
79p, Mon, May 6  Final Exam 