Semester:  Spring 2020, also offered on Fall 2020, Spring 2018, Fall 2017 and Fall 2016 
Time and place:  Tuesday and Thursday, 10.3011.45am, Lawson Building B155 
Instructor:  Jean Honorio, Lawson Building 2142J (Please send an email for appointments) 
TAs: 
Adarsh Barik, email: abarik at purdue.edu, Office hours: Wednesday, 9am11am, HAAS G50 Vinith Budde, email: budde at purdue.edu, Office hours: Monday, 1pm3pm, HAAS G50 
Date  Topic (Tentative)  Notes 
Tue, Jan 14  Lecture 1: perceptron (introduction)  Homework 0: due on Jan 16 at beginning of class  NO EXTENSION DAYS ALLOWED 
Thu, Jan 16  Lecture 2: perceptron (convergence), maxmargin classifiers, support vector machines (introduction)  Homework 0 due  NO EXTENSION DAYS ALLOWED 
Tue, Jan 21  Lecture 3: nonlinear feature mappings, kernels (introduction), kernel perceptron  Homework 0 solution 
Thu, Jan 23 
Lecture 4: SVM with kernels, dual solution Refs: [1] [2] (not mandatory to be read) 
Homework 1: due on Jan 30, 11.59pm EST 
Tue, Jan 28 
Lecture 5: oneclass problems (anomaly detection), oneclass SVM, multiway classification, direct multiclass SVM Refs: [1] [2] [3] [4] (not mandatory to be read) 

Thu, Jan 30 
Lecture 6: rating (ordinal regression), PRank, ranking, rank SVM Refs: [1] [2] (not mandatory to be read) 
Homework 1 due 
Tue, Feb 4  Lecture 7: linear and kernel regression, feature selection (information ranking, regularization, subset selection)  
Thu, Feb 6  —  
Tue, Feb 11  Lecture 8: ensembles and boosting  Homework 2: due on Feb 19, 11.59pm EST 
Thu, Feb 13  Lecture 9: performance measures, crossvalidation, biasvariance tradeoff, statistical hypothesis testing  
Tue, Feb 18  Lecture 10: model selection (VC dimension, generalization, structural risk minimization)  Homework 2 due on Wed, Feb 19 
Thu, Feb 20  Lecture 11: probability review (joint, marginal and conditional probability), independence, maximum likelihood estimation  
Tue, Feb 25  Lecture 12: generative probabilistic modeling, maximum likelihood estimation, decision boundary  
Thu, Feb 27  Lecture 13: mixture models, EM algorithm, convergence, model selection  Homework 3: due on Mar 3, 11.59pm EST 
Tue, Mar 3 
Lecture 14: active learning, kernel regression, Gaussian processes Refs: [1] (not mandatory to be read) 
Homework 3 due 
Thu, Mar 5  Lecture 15: dimensionality reduction, principal component analysis (PCA), kernel PCA  
Tue, Mar 10  MIDTERM (lectures 1 to 12)  10.30am11.45am, Lawson Building B155 
Thu, Mar 12  (midterm solution) 
Project plan due (see Assignments for details) [Word] or [Latex] format 
Tue, Mar 17  SPRING VACATION  
Thu, Mar 19  SPRING VACATION  
Tue, Mar 24  (lecture 15 continues)  
Thu, Mar 26 
Lecture 16: collaborative filtering (matrix factorization), structured prediction (maxmargin approach) Refs: [1] (not mandatory to be read) 
Homework 4: due on Mar 31, 11.59pm EST 
Tue, Mar 31  —  Homework 4 due 
Thu, Apr 2 
Lecture 17: Bayesian networks (motivation, examples, graph, independence) Refs: [1] [2] (not mandatory to be read) 

Tue, Apr 7 
Lecture 18: Bayesian networks (independence, equivalence, learning) Refs: [1] [2] [3, chapters 1620] (not mandatory to be read) 

Thu, Apr 9 
Lecture 19: Bayesian networks (introduction to inference), Markov random fields, factor graphs Refs: [1] [2] (not mandatory to be read) 
Preliminary project report due (see Assignments for details)  NO EXTENSION DAYS ALLOWED 
Tue, Apr 14  (lecture 19 continues)  
Thu, Apr 16 
Lecture 20: Markov random fields (inference, learning) Refs: [1] [2] [3, chapters 1620] (not mandatory to be read) 

Tue, Apr 21  (lecture 20 continues)  
Thu, Apr 23  Lecture 21: Markov random fields (inference in general graphs, junction trees)  Final project report due (see Assignments for details)  NO EXTENSION DAYS ALLOWED 
Tue, Apr 28  FINAL EXAM (lectures 13 to 21) 
Start: Tuesday April 28, 10.30am EST End: Wednesday April 29, 10.30am EST 
Thu, Apr 30  — 