CPSC 633: Machine Learning (Spring 2016)

Professor: Dr. Thomas R. Ioerger
Office: 322C HRBB
Email: ioerger@cs.tamu.edu
Office hours: Wed, 3:15-4:15, or by appointment

Class Time: Tues/Thurs, 3:55-5:10
Room: 108 CHEN
Course WWW page: http://www.cs.tamu.edu/faculty/ioerger/cs633-spr16/index.html
Textbook: Machine Learning. Tom Mitchell (1997). McGraw-Hill. pdf

Teaching Assistant: TBD office hours: TBD


Goals of the Course:

Machine learning is an important sub-area within AI, and is broadly applicable to many application areas within Computer Science. Machine learning can be viewed as methods for making systems adaptive (improving performance with experience), or alternatively, for augmenting the intelligence of knowledge-based systems via rule acquisition. In this course, we will examine and compare several different abstract models of learning, from hypothesis-space search, to function approximation (such as by gradient descent), to statistical inference (e.g. Bayesian), to the minimum description-length principle. Both theoretical issues (e.g. algorithmic complexity, hypothesis space bias) as well as practical issues (e.g. feature selection; dealing with noise and preventing overfit) will be covered.

Topics to be Covered:


Prerequisites

CPSC 420/625 - Introduction to Artificial Intelligence

We will be relying on core concepts in AI, especially heuristic search algorithms, optimization, and propositional logic Either the graduate or undergraduate AI class (or a similar course at another university) will count as satisfying this prerequisite.

In addition, the course will require some background in analysis of algorithms (big-O notation), and some familiarity with probability and statistics (e.g. standard deviation, confidence intervals, linear regression, Binomial distribution).

Projects and Exams

There will four or five programming projects and a final exam. The main work for the class will consist of several programming projects in which you will implement and test your own versions of several learning algorithms. These will not be group projects, so you will be expected to do your own work. Several databases will be provided for testing your algorithms (e.g. for accuracy).

Your grade at the end of the course will be based on a weighted average of points accumulated during the semester, 50% for projects and 50% for the final exam. The maximum cutoff for an A will be 90%, 80% for B, and 70% for C.

The late-assignment policy for homeworks and projects will be incremental: -5%/per day, down to a maximum of -50%. If the project is turned in anytime by the end of the semester, you can still get up to 50% (minus points marked off).


Schedule

Tues, Jan 19: first day of classtypes of machine learning; core conceptsCh. 1
Thurs, Jan 21: Version Spaces, inductive biasCh. 2, slides
Tues, Jan 26:week 2Decision TreesCh. 3, slides
Thurs, Jan 28:pruning (Mingers, 1989), slides
Tues, Feb 2:week 3
Thurs, Feb 4:Rule InductionCh 10.1-10.3; slides
Tues, Feb 9:week 4Empirical Evaluation Ch. 5, slides
Thurs, Feb 11:(class cancelled)
Tues, Feb 16:week 5 cross-validation, T-tests slides
Thurs, Feb 18:Neural NetworksCh. 4, slides
Tues, Feb 23:week 6
Thurs, Feb 25: Project 1 due
Tues, Mar 1:week 7Instance-Based LearningCh. 8 (8.1-8.2)
Thurs, Mar 3:NTgrowth, PCA slides
Tues, Mar 8:week 8Feature Selection slides
Thurs, Mar 10:
Mar 14-18:Spring BreakSpring Break
Tues, Mar 22:week 9Support Vector Machines (Burges, 1998), slides
Thurs, Mar 24: Project 2 due
Tues, Mar 29:week 10Bayesian Learning (hMAP, hML, MSE, MDL, BOC)Ch. 6
Thurs, Mar 31:Expectation Maximization
Tues, Apr 5:week 11guest lecture by Dr. James Caverlee:
Machine Learning Applications to Social Media
Thurs, Apr 7:Naive Bayes algorithm; Bayesian networks
Tues, Apr 12:week 12more on feature weighting slides
Thurs, Apr 14:HMMsRabiner (1989), slides
Tues, Apr 19:week 13ensemble classifiers: baggingBreiman (1996), slides
Thurs, Apr 21:boostingFreund and Schapire (1996), slides
Tues, Apr 26:week 14computational learning theoryCh. 7, slides
Thurs, Apr 28:Project 3 due
Tues, May 3class cancelled (last day of class)no office hours this week
Fri, May 6review session: 113 HRBB, 4:00-5:00
Mon, May 9 Final Exam: 1:00-3:00pm, 108 CHEN


Academic Integrity Statement and Policy

Aggie Code of Honor: An Aggie does not lie, cheat or steal, or tolerate those who do.
see: Honor Council Rules and Procedures


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