Semester:  Fall 2018 
Time and place:  Tuesday and Thursday, 1.30pm2.45pm, Mathematical Sciences Building 175 
Instructor:  Jean Honorio, Lawson Building 2142J (Please send an email for appointments) 
TAs: 
Hao Ding, email: ding209 at purdue.edu, Office hours: Friday 2pm4pm, HAAS G50 Ruijiu Mao, email: mao95 at purdue.edu, Office hours: Thursday 11am1pm, HAAS G50 Md Nasim, email: mnasim at purdue.edu, Office hours: Wednesday 2pm4pm, HAAS G50 Susheel Suresh, email: suresh43 at purdue.edu, Office hours: Tuesday 3pm5pm, HAAS G50 
Date  Topic (Tentative)  Notes 
Tue, Aug 21 
Lecture 0: linear algebra review Notes: [1] 
Python and Linear algebra in Python 
Thu, Aug 23 
Lecture 1: perceptron (introduction) Notes: [1] 

Tue, Aug 28 
Lecture 2: perceptron (convergence), maxmargin classifiers, support vector machines (introduction) Notes: [1] 

Thu, Aug 30  Lecture 3: nonlinear feature mappings, kernels (introduction), kernel perceptron  Homework 1: due on Sep 4, 11.59pm EST 
Tue, Sep 4 
Lecture 4: SVM with kernels, dual solution Notes: [1] Refs: [1] [2] (not mandatory to be read) 

Thu, Sep 6  (lecture continues)  Homework 2: due on Sep 11, 11.59pm EST 
Tue, Sep 11 
Lecture 5: oneclass problems (anomaly detection), oneclass SVM, multiway classification, direct multiclass SVM Notes: [1] Refs: [1] [2] [3] [4] (not mandatory to be read) 

Thu, Sep 13 
Lecture 6: rating (ordinal regression), PRank, ranking, rank SVM Notes: [1] Refs: [1] [2] (not mandatory to be read) 

Tue, Sep 18 
Lecture 7: linear and kernel regression, feature selection (information ranking, regularization, subset selection) Notes: [1] 
Homework 3: due on Sep 23, 11.59pm EST 
Thu, Sep 20 
Lecture 8: ensembles and boosting Notes: [1] 

Tue, Sep 25 
Lecture 9: performance measures, crossvalidation, statistical hypothesis testing Notes: [1] 
Homework 4: due on Sep 30, 11.59pm EST 
Thu, Sep 27  (lecture continues)  
Tue, Oct 2  Lecture 10: statistics review, model selection (introduction)  Homework 5: due on Oct 7, 11.59pm EST 
Thu, Oct 4 
Lecture 11: model selection (growth function, VC dimension) Notes: [1] 

Tue, Oct 9  OCTOBER BREAK  
Thu, Oct 11  Lecture 12: dimensionality reduction, principal component analysis (PCA)  
Tue, Oct 16  MIDTERM (lectures 1 to 11)  1.30pm2.45pm, Mathematical Sciences Building 175 
Thu, Oct 18  Midterm solution (01, 02, 03)  Homework 6: due on Oct 23, 11.59pm EST 
Tue, Oct 23  Case Study 1  
Thu, Oct 25  Case Study 2  
Tue, Oct 30  Case Study 3 
Project plan due (see Assignments for details) [Word] or [Latex] format 
Thu, Nov 1  Lecture  Homework 7 (TENTATIVE): due on Nov 6, 11.59pm EST 
Tue, Nov 6  Lecture  
Thu, Nov 8  Lecture  
Tue, Nov 13  Lecture  
Thu, Nov 15  Lecture  Preliminary project report due (DETAILS LATER) 
Tue, Nov 20  Lecture  
Thu, Nov 22  THANKSGIVING VACATION  
Tue, Nov 27  Lecture  
Thu, Nov 29  FINAL EXAM (lectures 12 to 22)  1.30pm2.45pm, Mathematical Sciences Building 175 
Sat, Dec 1  —  Final project report due (DETAILS LATER) 
Tue, Dec 4  (final solution)  
Thu, Dec 6  — 