[Fall 2015 – 16:198:535)] Pattern Recognition: Theory & Applications

Schedule / Syllabus (Subject to Change)

 

Lec. #

Day

Date

Topic

Readings

1

Thu

Sep 3

Introduction & Overview

á      http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf

á      http://eliassi.org/ml-science-2015.pdf

—

Mon

Sep 7

No class (Labor Day)

2

Tue

Sep 8

(Designation Day)

Association Rules &

Frequent Itemsets

á      Chapter 14.1 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

á      Chapter 14.2-14.2.3 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

á      Chapter 6 of http://www.mmds.org/#book

3

Thu

Sep 10

4

Mon

Sep 14

Density Estimation

á      Chapters 6.6 through 6.9 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

á      http://ned.ipac.caltech.edu/level5/March02/Silverman/Silver_contents.html

5

Thu

Sep 17

—

Mon

Sep 21

Homework #1 assigned

Project proposals and in-class pitches assigned

6

Mon

Sep 21

K-means

á      Chapters 9.1 and 9.2 of http://robotics.stanford.edu/~nilsson/MLBOOK.pdf

á      Chapters 14.3.1 through 14.3.6 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

á      Chapter 7 of http://www.mmds.org/#book

á      https://www.cs.rutgers.edu/~mlittman/courses/lightai03/jain99data.pdf

á      Chapters 13.1 and 13.2 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

7

Thu

Sep 24

Gaussian Mixtures & Expectation Maximation & Factor Analysis

á      Mixture of Gaussians: http://cs229.stanford.edu/notes/cs229-notes7b.pdf

á      The EM Algorithm: http://cs229.stanford.edu/notes/cs229-notes8.pdf

á      Factor Analysis: http://cs229.stanford.edu/notes/cs229-notes9.pdf

á      Chapters 14.3.7 through 14.3.9 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

8

Mon

Sep 28

K-medoids & Hierarchical Clustering

á      Chapter 14.3.10 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

á      Chapter 14.3.12 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

á      Chapter 9.3 of http://robotics.stanford.edu/~nilsson/MLBOOK.pdf

9

Thu

Oct 1

Evaluation Metrics & Practical Issues

á      http://web.itu.edu.tr/sgunduz/courses/verimaden/paper/validity_survey.pdf

á      Chapter 14.3.11 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

—

Fri

Oct 2

Homework #1 due at 11:30 PM Eastern

10

Mon

Oct 5

Distance/Similarity Measures & Metric Learning

á      http://web.cse.ohio-state.edu/~kulis/pubs/ftml_metric_learning.pdf

á      Check out the Encyclopedia of Distances on this courseÕs Sakai site (under Resources).

—

Thu

Oct 8

Homework #2 assigned

11

Thu

Oct 8

Principal Component Analysis (PCA) & Singular Value Decomposition (SVD)

á      Chapter 14.5 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

á      Chapter 11 of http://www.mmds.org/#book

 

(Lecturer: Chetan Tonde)

12

Mon

Oct 12

Spectral Clustering & Graph Clustering

á      http://ai.stanford.edu/~ang/papers/nips01-spectral.pdf

á      http://www.cs.columbia.edu/~jebara/4772/papers/Luxburg07_tutorial.pdf

á      [Optional] http://arxiv.org/pdf/0906.0612.pdf

—

Wed

Oct 14

Homework #1 graded

Note: Warning grades will be issued by Fri Oct 16.

13

Thu

Oct 15

Kernel Principal Components & Independent Component Analysis (ICA) & Canonical Correlation Analysis (CCA) & PageRank

á      Chapter 14.5 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

á      ICA: Chapter 14.7 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

á      CCA: https://www.cs.cmu.edu/~tom/10701_sp11/slides/CCA_tutorial.pdf

á      PageRank:

o   Chapter 5 of http://www.mmds.org/#book

o   Chapter 14.10 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

o   https://www.cs.purdue.edu/homes/dgleich/publications/Gleich%202015%20-%20prbeyond.pdf

14

Mon

Oct 19

—

Tue

Oct 20

Homework #2 due at 11:30 PM Eastern

—

Wed

Oct 21

Two-page project proposals due at 11:30 PM Eastern

15

Thu

Oct 22

In-class project pitches

16

Mon

Oct 26

Recommendation Systems

á      http://infolab.stanford.edu/~ullman/mmds/ch9.pdf

á      http://eliassi.org/papers/chaney-recsys15.pdf

 

á      (Lecturer: Chetan Tonde)

17

Thu

Oct 29

Midterm exam (Proctored by Chetan Tonde)

—

Sun

Nov 1

Project proposals & pitches graded

Project presentations and reports assigned

18

Mon

Nov 2

Latent Variable Models &

Probabilistic Topic Models

á      http://research.microsoft.com/pubs/67187/bishop-latent-erice-99.pdf

á      http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf

á      http://www.cs.princeton.edu/~blei/papers/Blei2011.pdf

á      http://www.cs.columbia.edu/~blei/papers/BleiLafferty2009.pdf

19

Thu

Nov 5

—

Fri

Nov 6

Homework #2 graded

20

Mon

Nov 9

Latent Variable Models &

Probabilistic Topic Models (continued)

á      http://www.cs.berkeley.edu/~jordan/papers/variational-intro.pdf

á      http://www.cs.ubc.ca/~arnaud/andrieu_defreitas_doucet_jordan_intromontecarlomachinelearning.pdf

á      https://www.ee.washington.edu/techsite/papers/documents/UWEETR-2010-0006.pdf

21

Thu

Nov 12

22

Mon

Nov 16

Matrix Factorization

á      Chapter 14.6 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

á      http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf

23a

Thu

Nov 19

Tensor Factorization

á      http://www.sandia.gov/~tgkolda/pubs/pubfiles/TensorReview.pdf

23b

Thu

Nov 19

Midterm exam graded and returned at the end of lecture

24

Mon

Nov 23

Model Selection

á      Chapter 7 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

25

Thu

Nov 26

No class – Thanksgiving Holiday

26

Mon

Nov 30

Model Selection (continued)

á      Chapter 7 of http://statweb.stanford.edu/~tibs/ElemStatLearn/

27

Thu

Dec 3

Theory of Clustering

á      http://www.cs.cornell.edu/home/kleinber/nips15.pdf

á      http://papers.nips.cc/paper/3491-measures-of-clustering-quality-a-working-set-of-axioms-for-clustering.pdf

28

Mon

Dec 7

In-class project presentations

29

Thu

Dec 10

In-class project presentations

Last day of classes

—

Sun

Dec 13

Project presentations graded

—

Fri

Dec 18

Project reports due at 11:30 PM Eastern

—

Mon

Dec 21

Project reports graded and final grades released.