Semester:  Spring 2018, also offered on Fall 2020, Spring 2020, 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: Thursday, 3pm5pm, HAAS G50 Shraddha Sahoo, email: sahoo0 at purdue.edu, Office hours: Monday, 3pm5pm, HAAS G50 
Date  Topic (Tentative)  Notes 
Tue, Jan 9 
Lecture 1: perceptron (introduction) Notes: [1] 
Homework 0: due on Jan 11 at beginning of class  NO EXTENSION DAYS ALLOWED 
Thu, Jan 11 
Lecture 2: perceptron (convergence), maxmargin classifiers, support vector machines (introduction) Notes: [1] 
Homework 0 due  NO EXTENSION DAYS ALLOWED 
Tue, Jan 16  Lecture 3: nonlinear feature mappings, kernels (introduction), kernel perceptron  Homework 0 solution 
Thu, Jan 18  —  
Tue, Jan 23 
Lecture 4: SVM with kernels, dual solution Notes: [1] Refs: [1] [2] (not mandatory to be read) 
Homework 1: due on Jan 30, 11.59pm EST 
Thu, Jan 25 
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) 

Tue, Jan 30 
Lecture 6: rating (ordinal regression), PRank, ranking, rank SVM Notes: [1] Refs: [1] [2] (not mandatory to be read) 
Homework 1 due 
Thu, Feb 1 
Lecture 7: linear and kernel regression, feature selection (information ranking, regularization, subset selection) Notes: [1] 

Tue, Feb 6 
Lecture 8: ensembles and boosting Notes: [1] 

Thu, Feb 8 
Lecture 9: model selection (finite hypothesis class) Notes: [1] Refs: [1] (not mandatory to be read) 
Homework 2: due on Feb 15, 11.59pm EST 
Tue, Feb 13 
Lecture 10: model selection (growth function, VC dimension, PAC Bayesian bounds) Notes: [1] 

Thu, Feb 15 
Lecture 11: performance measures, crossvalidation, biasvariance tradeoff, statistical hypothesis testing Notes: [1] 
Homework 2 due 
Tue, Feb 20 
Lecture 12: dimensionality reduction, principal component analysis (PCA), kernel PCA Notes: [1] 
Homework 3: due on Feb 27, 11.59pm EST 
Thu, Feb 22 
Lecture 13: generative probabilistic modeling, maximum likelihood estimation, mixture models, EM algorithm (introduction) Notes: [1] 

Tue, Feb 27 
Lecture 14: mixture models, EM algorithm, convergence, model selection Notes: [1] 
Homework 3 due 
Thu, Mar 1 
Lecture 15: active learning, kernel regression, Gaussian processes Refs: [1] (not mandatory to be read) 

Tue, Mar 6  MIDTERM (lectures 1 to 12)  10.30am11.45am, Lawson Building B155 
Thu, Mar 8  (midterm solution) 
Project plan due (see Assignments for details) [Word] or [Latex] format 
Tue, Mar 13  SPRING VACATION  
Thu, Mar 15  SPRING VACATION  
Tue, Mar 20 
Lecture 16: collaborative filtering (matrix factorization), structured prediction (maxmargin approach) Notes: [1] Refs: [1] (not mandatory to be read) 

Thu, Mar 22  —  
Tue, Mar 27 
Lecture 17: Bayesian networks (motivation, examples, graph, independence) Notes: [1] Refs: [1] [2] (not mandatory to be read) 

Thu, Mar 29 
Lecture 18: Bayesian networks (independence, equivalence, learning) Notes: [1] Refs: [1] [2] [3, chapters 1620] (not mandatory to be read) 
Preliminary project report due (see Assignments for details) 
Tue, Apr 3 
Lecture 19: Bayesian networks (introduction to inference), Markov random fields, factor graphs Notes: [1] Refs: [1] [2] (not mandatory to be read) 

Thu, Apr 5 
Lecture 20: Markov random fields (inference, learning) Notes: [1] Refs: [1] [2] [3, chapters 1620] (not mandatory to be read) 

Tue, Apr 10  —  
Thu, Apr 12  (lecture continues)  Final project report due (see Assignments for details) 
Tue, Apr 17 
Lecture 21: Markov random fields (inference in general graphs, junction trees) Notes: [1] 

Thu, Apr 19  FINAL EXAM (lectures 13 to 21)  10.30am11.45am, Lawson Building B155 
Tue, Apr 24  —  
Thu, Apr 26  — 