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 
Lecture 15: ensemble methods: bagging, boosting, bias/variance tradeoff (iClicker: attendance) 
Homework 6: due on Oct 31, 11.59pm EST 
Tue, Oct 29 
Case Study 3 (iClicker: attendance) 
Project plan due (see Assignments for details) [Word] or [Latex] format 
Thu, Oct 31 
Lecture 16: clustering, kmeans, hierarchical clustering (iClicker: attendance) 
Homework 6 due 
Tue, Nov 5 
Lecture 17: clustering, mixture models, expectationmaximization (EM) algorithm (iClicker: attendance) 
Homework 7: due on Nov 12, 11.59pm EST 
Thu, Nov 7 
Lecture 18: anomaly detection, oneclass support vector machines (iClicker: attendance) 

Tue, Nov 12 
Lecture 19: Bayesian networks (independence) (iClicker: attendance) 
Homework 7 due 
Thu, Nov 14 
Lecture 20: pattern discovery, association rules, frequent itemsets (iClicker: attendance) 
Preliminary project report, due on Nov 16, 11.59pm EST 
Tue, Nov 19 
Lecture 21: feature selection (univariate/multivariate, filter/wrapper/embedded methods, L1norm regularization) (iClicker: attendance) 

Thu, Nov 21 
Lecture 22: data quality, preprocessing, visualization, distances (iClicker: attendance) 

Tue, Nov 26  FINAL EXAM (lectures 13 to 21, all case studies)  4.30pm5.45pm, Electrical Engineering Building 170 
Thu, Nov 28  THANKSGIVING VACATION  
Tue, Dec 3  Final exam solution  Final project report, due on Dec 3, 11.59pm EST 
Thu, Dec 5  — 