Semester:  Fall 2019, also offered on Fall 2018 
Time and place:  Tuesday and Thursday, 4.30pm5.45pm, Electrical Engineering Building 170 
Instructor:  Jean Honorio, Lawson Building 2142J (Please send an email for appointments) 
TAs: 
Jiayi Liu, email: liu2861 at purdue.edu, Office hours: Friday 10amnoon, HAAS G50 Susheel Suresh, email: suresh43 at purdue.edu, Office hours: Wednesday 35pm, HAAS G50 Vinith Budde, email: budde at purdue.edu, Office hours: Monday 3pm5pm, HAAS G50 
Date  Topic (Tentative)  Notes 
Tue, Aug 20  Lecture 1: introduction  Python 
Thu, Aug 22  Lecture 2: probability review (joint, marginal and conditional probabilities)  
Tue, Aug 27  Lecture 3: statistics review (independence, maximum likelihood estimation)  
Thu, Aug 29 
Lecture 4: linear algebra review (iClicker: attendance) 
Linear algebra in Python Homework 1: due on Sep 5, at end of lecture 
Tue, Sep 3  Lecture 5: elements of data mining and machine learning algorithms  
Thu, Sep 5 
Lecture 6: linear classification, perceptron (iClicker: quiz 1) 
Homework 1 due Homework 1 solution 
Tue, Sep 10 
Lecture 7: perceptron (convergence), support vector machines (introduction) (iClicker: attendance) 
Homework 2: due on Sep 17, 11.59pm EST 
Thu, Sep 12 
Lecture 8: generative probabilistic modeling, maximum likelihood estimation, classification (iClicker: attendance) 

Tue, Sep 17 
Lecture 9: generative probabilistic classification (naive Bayes), nonparametric methods (nearest neighbors) (iClicker: attendance) 
Homework 2 due 
Thu, Sep 19 
Lecture 10: nonparametric methods (classification trees) (iClicker: quiz 2) 
Homework 3: due on Sep 26, 11.59pm EST 
Tue, Sep 24 
Case Study 1 (iClicker: attendance) 

Thu, Sep 26  Lecture 11: performance measures, crossvalidation, statistical hypothesis testing  Homework 3 due 
Tue, Oct 1 
Lecture 12: model selection and generalization (VC dimension) (iClicker: attendance) 
Homework 4: due on Oct 10, 11.59pm EST 
Thu, Oct 3 
Case Study 2 (iClicker: attendance) 

Tue, Oct 8  OCTOBER BREAK  
Thu, Oct 10  Lecture 13: dimensionality reduction, principal component analysis (PCA)  Homework 4 due 
Tue, Oct 15  MIDTERM (lectures 1 to 12, all case studies) 
4.30pm5.45pm, Electrical Engineering Building 170 Homework 5: due on Oct 22, 11.59pm EST 
Thu, Oct 17 
Midterm solution (iClicker: attendance) 

Tue, Oct 22 
Lecture 14: nonlinear feature mappings, kernels, kernel perceptron, kernel support vector machines (iClicker: attendance) 
Homework 5 due 
Thu, Oct 24  TBD  Homework 6: due on Oct 31 
Tue, Oct 29  Case Study 3 
Project plan due (see Assignments for details) [Word] or [Latex] format 
Thu, Oct 31  TBD  
Tue, Nov 5  TBD  Homework 7: due on Nov 12 
Thu, Nov 7  Case Study 4  
Tue, Nov 12  TBD  Preliminary project report, due on Nov 16 
Thu, Nov 14  TBD  
Tue, Nov 19  TBD  
Thu, Nov 21  TBD  
Tue, Nov 26  FINAL EXAM (lectures 13 to 21, all case studies) 
4.30pm5.45pm, Electrical Engineering Building 170 Final project report, due on Nov 30 
Thu, Nov 28  THANKSGIVING VACATION  
Tue, Dec 3  Final exam solution  
Thu, Dec 5  — 