Semester:  Fall 2017, also offered on Fall 2016 by me, and on Fall 2015 by Dan Goldwasser 
Time and place:  Tuesday and Thursday, 12pm1.15pm, Wetherill Lab 320 
Instructor:  Jean Honorio, Lawson Building 2142J (Please send an email for appointments) 
TAs: 
Chang Li, email: li1873 at purdue.edu, Office hours: Monday, noon2pm, HAAS G50 Adarsh Barik, email: abarik at purdue.edu, Office hours: Wednesday, 1:203:20pm, HAAS G50 
Date  Topic (Tentative)  Notes 
Tue, Aug 22  Lecture 1: perceptron (introduction)  Homework 0: due on Aug 24 at beginning of class  NO EXTENSION DAYS ALLOWED 
Thu, Aug 24  Lecture 2: perceptron (convergence), maxmargin classifiers, support vector machines (introduction)  Homework 0 due  NO EXTENSION DAYS ALLOWED 
Tue, Aug 29  Lecture 3: nonlinear feature mappings, kernels (introduction), kernel perceptron  Homework 0 solution 
Thu, Aug 31 
Lecture 4: SVM with kernels, dual solution Refs: [1] [2] (not mandatory to be read) 
Homework 1: due on Sep 7, 11.59pm EST 
Tue, Sep 5 
Lecture 5: oneclass problems (anomaly detection), oneclass SVM, multiway classification, direct multiclass SVM Refs: [1] [2] [3] [4] (not mandatory to be read) 

Thu, Sep 7 
Lecture 6: rating (ordinal regression), PRank, ranking, rank SVM Refs: [1] (not mandatory to be read) 
Homework 1 due 
Tue, Sep 12  Lecture 7: linear and kernel regression, feature selection (information ranking, regularization, subset selection)  
Thu, Sep 14  Lecture 8: ensembles and boosting  Homework 2: due on Sep 21, 11.59pm EST 
Thu, Sep 19 
Lecture 9: model selection (finite hypothesis class) Refs: [1] (not mandatory to be read) 

Tue, Sep 21  —  Homework 2 due 
⋮  ⋮  ⋮ 
Thu, Dec 14  FINAL EXAM  3.30pm5.30pm at WTHR 172 