CPSC 633-600 Machine Learning:
Spring 2006


NEWS: 5/12/06, 12:23PM (Fri)
  • [5/12] Final letter grades uploaded: grades/.
  • -----------------------
  • All past lecture notes and homework/solution are available in the lectures/ directory.
Read-Only Bulletin Board.: 2/9/06, 11:27AM (Thu)

Page last modified: 4/7/06, 09:36AM Friday.

General Information Resources Reading List Weekly Schedule Lecture Notes

I. General Information


Dr. Yoonsuck Choe
Email: choe(a)tamu.edu
Office: HRBB 322B
Phone: 845-5466
Office hours: 2:30-4pm, MW


Yingwei Yu
Email: yingweiy(a)cs.tamu.edu
Office: HRBB 322A
Phone: 845-5481
Office hours: TR 10:30-12:00


CPSC 420, 625, or consent of instructor.


MWF 12:40--1:30pm HRBB 113.


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.


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.


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 windows system in the departmental lab.

Topics to be covered:

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


  1. 3 written + programming assignments, 18% each = 54%
  2. Midterm and final exam, 15% each = 30%
  3. Mini project 16%
Grading will be on the absolute scale. 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.


  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

Notices and Dues
1 1/16 MLK Day (Holiday)        
1 1/18 Introduction 1.1--1.2     slide01.pdf
1 1/20 Introduction 1.3--1.5     slide01.pdf
2 1/23 Concept learning 2.1--2.4     slide02.pdf
2 1/25 Concept learning 2.5--2.6     slide02.pdf
2 1/27 Concept learning 2.7--2.8     slide02.pdf
3 1/30 Decision tree 3.1--3.4     slide03.pdf
3 2/1 Decision tree 3.5--3.8     slide03.pdf
3 2/3 ANN 4.1--4.4 HW1 Announced hw1.pdf   slide04.pdf
4 2/6 Guest lecture Heejin Lim, on delay compensation   Project PI meeting at NIH (Make up TBA) slide04.pdf
4 2/8 ANN 4.5--4.6     slide04.pdf
4 2/10 ANN 4.7--4.9     slide04.pdf
5 2/13 ANN (applications) TBA     slide04.pdf
5 2/15 Evaluating hypotheses 5.1--5.3     slide05.pdf
5 2/17 Evaluating hypotheses 5.4--5.7     slide05.pdf
6 2/20 Bayesian learning 6.1--6.4   HW1 Due (in class: postponed to 2/28) slide06.pdf
6 2/22 Bayesian learning 6.5--6.9 HW2 announced: hw2.pdf   slide06.pdf
6 2/24 Bayesian learning 6.11--6.13     slide06.pdf
7 2/27 Bayesian learning 6.11--6.13     slide06.pdf
7 3/1 Bayesian learning 6.11--6.13     slide06.pdf
7 3/3 Midterm exam        
8 3/6 Bayesian learning 6.11--6.13     slide06.pdf
8 3/8 Bayesian learning 6.11--6.13     slide06.pdf
8 3/10 Reinforcement learning 13.1--13.3.3     slide07.pdf
9 3/13 Spring break        
9 3/15 Spring break        
9 3/17 Spring break        
10 3/20 Reinforcement learning 13.3.4--13.5     slide07.pdf
10 3/22 Reinforcement learning 13.6--13.8 HW3 TBA HW2 due, in class slide07.pdf
10 3/24 Guest lecture Yingwei Yu, on intra-class clustering   WAM-BAMM workshop  
11 3/27 Reinforcement learning (autonomous semantics)       slide08.pdf
11 3/29 Reinforcement learning (autonomous semantics)       slide08.pdf
11 3/31 No Class Out-of-class
final exam review
(details TBA)
  NIH blueprint meeting  
12 4/3 Imitation learning Rao et al. (2004) [PDF]     slide09.pdf
12 4/5 Genetic algorithms 9.1--9.3     slide10.pdf
12 4/7 Genetic algorithms 9.4--9.8     slide10.pdf
13 4/10 Genetic algorithms (neuroevolution) TBA   HW3 due, in class slide11.pdf
13 4/12 Instance-based learning 8.1--8.3     slide12.pdf
13 4/14 No class Reading day      
14 4/17 Instance-based learning 8.4--8.7     slide12.pdf
14 4/19 Computational learning theory 7.1--7.3     slide13.pdf
14 4/21 Computational learning theory 7.4--7.6     slide13.pdf
15 4/24 Learning in biological vision TBA      
15 4/26 Project presentation        
15 4/28 Project presentation        
16 5/1 Project presentation        
165/2Project presentation and Course wrapup  

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