CPSC 633-600 Machine Learning:
Spring 2009

Syllabus

NEWS: 5/14/09, 11:20AM (Thu)
  • [05/14/09] Final grades posted: grade sheet.

  • [05/03/09] Final project report due by 5/8 (Fri) 1pm 5pm to HRBB 322B.
  • [05/01/09] No office hour today.
  • [05/01/09] slide14 uploaded. Bring to class.

  • [04/28/09] Homework 3 grades posted: grade sheet.
  • [04/27/09] Final exam results posted: grade sheet.
  • [04/24/09] Homework 3 solution posted hw3-sol.
  • [04/22/09] Project presentation: teams, themes, and tentative schedule posted.
  • [04/22/09] Online course evaluation: pica.tamu.edu
  • [04/21/09] slide13 uploaded. Bring to class.
  • [04/16/09] slide12 uploaded. Bring to class.
  • [04/20/09] Final exam: postponed to Monday 4/27, in class. Material: slide07 to slide12.
  • [04/16/09] slide12 uploaded. Bring to class.
  • [04/16/09] Old announcements moved to the archive.

  • [04/15/09] twogaussian.m example for Octave.
  • [04/15/09] SLATE evaulation today.
  • [04/15/09] slide11 uploaded. Bring to class.
  • [04/15/09] Make-up session this evening (Wed) 6pm, in HRBB 302.
  • [04/11/09] homework 3 posted. Due date 4/20/09, in class
  • [03/26/09] Term project proposal due date postponed to 4/3 Friday, in class.
  • [03/25/09] grades posted (hw1, hw2, and midterm).
  • [03/25/09] slide10 uploaded.
  • [03/23/09] Office hour today: 10:00am-10:20am.
  • [03/22/09] slide09 uploaded.
  • [03/22/09] Term project ideas and details: Read-Only Board.
  • For older announcements, see the archive
Read-Only Bulletin Board.: 1/22/09, 03:46PM (Thu)

Page last modified: 1/21/09, 08:56AM Wednesday.

General Information Resources Reading List Weekly Schedule Lecture Notes

I. General Information

Instructor:

Dr. Yoonsuck Choe
Email: choe(a)tamu.edu
Office: HRBB 322B
Phone: 845-5466
Office hours: 10:10am-11:10am, MWF

TA: N/A

Prerequisite/Restrictions:

CPSC 420, 625, or consent of instructor.

Lectures:

MWF 9:10am–10:00am HRBB 126.

Introduction:

Machine learning is the study of self-modifying computer systems that can acquire new knowledge and improve their own performance; survey machine learning techniques, which include induction from examples, Bayesian learning, artificial neural networks, instance-based learning, genetic algorithms, reinforcement learning, unsupervised learning, and biologically motivated learning algorithms. Prerequisite: CPSC 420 or 625.

Goal:

The goal of this course is to

  1. learn various problems and solution strategies in machine learning.
  2. learn practical methodology for applying ML algorithms to problem domain of your choice.

Textbook:

Administrative Trivia:

  1. Computer accounts: if you do not have a unix account, ask for one on the CS web page.
  2. Programming languages permitted: C/C++, Java, or Matlab (or octave), and must be executable on CS unix hosts or any public system in the departmental lab.

Topics to be covered:

See the Weekly Schedule section for more details. The content will closely reflect Mitchell (1997).

Grading:

  1. 3 assignments (including written and programming components), 18% each = 54%
  2. 2 exams (in class), 15% each = 30%
  3. Mini project 16%
The cutoff for an `A' will be at most 90% of total score, 80% for a `B', 70% for a `C', and 60% for a `D'. However, these cutoffs might be lowered at the end of the semester to accomodate the actual distribution of grades.

Academic Integrity Statement:

AGGIE HONOR CODE: An Aggie does not lie, cheat, or steal or tolerate those who do.

Upon accepting admission to Texas A&M University, a student immediately assumes a commitment to uphold the Honor Code, to accept responsibility for learning, and to follow the philosophy and rules of the Honor System. Students will be required to state their commitment on examinations, research papers, and other academic work. Ignorance of the rules does not exclude any member of the TAMU community from the requirements or the processes of the Honor System.

For additional information please visit: http://www.tamu.edu/aggiehonor/

Local Course Policy:

Students with Disabilities:

The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact the Department of Student Life, Services for Students with Disabilities, in Cain Hall or call 845-1637.

Resources:

  1. UCI Machine Learning Repository: datasets to test machine learning algorithms.
  2. Research resources page
  3. Ganeral reading list (u: p: ): includes short blurb about how to find, read, and critique others' work. This list is not the course reading list.

III. Weekly Schedule and Class Notes

Week
Date
Topic
Reading
Assignments
Notices and Dues
Notes
1 1/19 MLK Day (Holiday)        
1 1/21 Introduction 1.1–1.2, 1.3–1.5     slide01.pdf
1 1/23 Introduction
Concept learning
2.1–2.4     slide02.pdf
2 1/26 Concept learning 2.5–2.6     slide02.pdf
2 1/28 Concept learning 2.7–2.8     slide02.pdf
2 1/30 Concept learning 2.7–2.8     slide02.pdf
3 2/2 ANN 4.1–4.4     slide03.pdf
3 2/4 ANN 4.5–4.6     slide03.pdf
3 2/6 ANN 4.7–4.9     slide03.pdf
4 2/9 ANN 4.7–4.9 Homework 1 announced   slide03.pdf
4 2/11 ANN 4.7–4.9     slide03.pdf
4 2/13 ANN (applications)       slide03.pdf
5 2/16 Decision tree 3.1–3.4     slide04.pdf
5 2/18 Decision tree 3.5–3.8 Homework 2 announced Homework 1 due slide04.pdf
5 2/20 Reinforcement learning 13.1–13.3.3     slide05.pdf
6 2/23 Reinforcement learning 13.3.4–13.5,13.6–13.8     slide05.pdf
6 2/25 Reinforcement learning Rao et al. (2004) on imitation learning     slide06.pdf
6 2/27 Genetic algorithms 9.1–9.3     slide07.pdf
7 3/2 Genetic algorithms 9.4–9.8     slide07.pdf
7 3/4 Genetic algorithms 9.4–9.8     slide07.pdf
7 3/6 Genetic algorithms
(neuroevolution)
TBA     slide08.pdf
8 3/9 Midterm Exam        
8 3/11 Genetic algorithms
(neuroevolution)
TBA     slide08.pdf
8 3/13 Guest lecture Jaerock Kwon   Trip to DC (PI meeting)  
9 3/16 Spring break        
9 3/18 Spring break        
9 3/20 Spring break        
10 3/23 Evaluating hypotheses 5.1–5.3   Homework 2 due slide09.pdf
10 3/25 Evaluating hypotheses 5.4–5.7     slide09.pdf
10 3/27 Bayesian learning 6.1–6.4     slide10.pdf
11 3/30 Bayesian learning 6.1–6.4     slide10.pdf
11 4/1 Guest lecture
Gary Wan (TAMU librarian)
Presentation on various library resources   Conference trip  
11 4/3 Class canceled (due to canceled flight)     Make-up session: Wed 4/15, 6pm in HRBB 302  
12 4/6 Bayesian learning 6.5–6.9     slide10.pdf
12 4/8 Bayesian learning 6.11–6.13     slide10.pdf
12 4/10 No class Reading day Homework 3 announced    
13 4/13 Bayesian learning 6.11–6.13     slide10.pdf
13 4/15 Bayesian learning 6.11–6.13   Make-up session this evening at 6pm slide10.pdf
13 4/17 Computational learning theory 7.1–7.3,7.4–7.6     slide11.pdf
14 4/20 Instance-based learning 8.1–8.3; 8.4–8.7   Homework 3 due slide12.pdf
14 4/22 Instance-based learning 8.1–8.3; 8.4–8.7     slide12.pdf
14 4/24 Learning in biological vision Miikkulainen et al. (2005)     slide13.pdf
15 4/27 Final exam        
15 4/29 Learning in biological vision Miikkulainen et al. (2005)     slide13.pdf
15 5/1 Unsupervised learning       slide14.pdf
16 5/4 Project presentation        
165/5Project presentation   


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