Semester:  Fall 2018, also offered on Spring 2021 and Fall 2019 
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), 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 Notes: [1] 

Thu, Sep 6  (lecture continues)  Homework 2: due on Sep 11, 11.59pm EST 
Tue, Sep 11 
Lecture 5: anomaly detection (oneclass SVM), multiway classification Notes: [1] 

Thu, Sep 13 
Lecture 6: rating (ordinal regression), PRank, ranking, rank SVM Notes: [1] 

Tue, Sep 18 
Lecture 7: 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 (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  Lecture 13: probability review (joint, marginal and conditional probabilities) 
Project plan due (see Assignments for details) [Word] or [Latex] format 
Thu, Nov 1  Lecture 14: statistics review (independence, maximum likelihood estimation)  
Tue, Nov 6  Lecture 15: generative probabilistic modeling, maximum likelihood estimation, classification  Homework 7: due on Nov 13, at end of lecture 
Thu, Nov 8  Lecture 16: clustering, mixture models, expectationmaximization (EM) algorithm  
Tue, Nov 13  Case Study 3  Homework 7 solution 
Thu, Nov 15 
Lecture 17: Bayesian networks (independence) Refs: [1] (not mandatory to be read) 
Preliminary project report, due on Nov 16, 11.59pm EST 
Tue, Nov 20  Lecture 18: generative probabilistic classification (naive Bayes), nonparametric methods (nearest neighbors)  
Thu, Nov 22  THANKSGIVING VACATION  
Tue, Nov 27  Lecture 19: nonparametric methods (classification trees)  
Thu, Nov 29  FINAL EXAM (lectures 12 to 19, all case studies) 
1.30pm2.45pm, Mathematical Sciences Building 175 Final project report, due on Dec 1, 11.59pm EST 
Tue, Dec 4  Final exam solution  
Thu, Dec 6  — 