CPSC 633-600 Machine Learning:
Spring 2011

Syllabus

NEWS: 4/21/11, 11:37AM (Thu)
  • [04/21/11] old-final uploaded
  • [04/20/11] slide13 uploaded
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  • [04/18/11] slide12 uploaded (won't be covered in class).
  • [04/18/11] slide11 uploaded.
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  • [04/11/11] hw3 uploaded. Due 4/21, in class.
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  • [04/04/11] slide10 uploaded.
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  • [03/30/11] Project teams and topics uploaded.
  • [03/30/11] slide09 uploaded.
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  • [03/24/11] Project resources: Xbraitenberg (Debugging info), Khepera robot simulator, UCI ML repository. Ken Stanley's page
  • [03/24/11] Project instructions (updated: 11:13am, 3/24). Proposal due date 3/29 3/31, in class.
  • [03/23/11] slide08 uploaded.
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  • [03/10/11] slide07 uploaded.
  • [03/07/11] Homework 2 part 1 solution posted hw2-sol.pdf. This is password protected. Do not distribute this in anyway.
  • [03/07/11] Slide05 correction posted. correction.pdf.
  • [03/07/11] Homework 1 solution posted hw1-sol.pdf. This is password protected. Do not distribute this in anyway.
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  • [03/03/11] Homework 2, part 2 (worth 25 points): program -- write a program to solve Alpaydin chapter 18, exercise #5. Due date: 3/22/2011, in class. See read-only board.
  • [03/01/11] slide06 uploaded.
  • [02/26/11] hw2 (part 1) uploaded. Due 3/7, during TA's office hour (hardcopy). Programs must be submitted via CSNET.
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  • [02/23/11] Exam #1 will cover slide01, slide01b, slide02, slide03, sldie04, and slide05.
  • [02/23/11] old-quiz2 uploaded (decision tree and RL).
  • [02/22/11] Homework late submission policy: -10 points/day (11:59am will be the cut-off time each day). On weekends, submit assignment via email (scan if needed) to the TA.
  • [02/21/11] old-midterm uploaded (password protected).
  • [02/21/11] slide05 uploaded.
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  • [02/14/11] TA's discussion forum
  • [02/14/11] slide04 uploaded.
  • [02/11/11] hw1 uploaded. Due 2/24, in class (hardcopy). Programs must be submitted via CSNET.
  • [01/24/11] slide03 uploaded.
  • [01/18/11] slide02 uploaded. This one integrates old slides based on Mitchell and new slides from Alpaydin.
  • [01/17/11] slide sets that end with "b" (e.g., slide01b.pdf) are excerpts from Alpaydin's slides from the textbook web page.
  • [01/17/11] Print out syllabus and slide01, slide01b and bring to class.
  • [01/17/11] Course web page goes online.
  • ---------------------------------------------------------
  • For older announcements, see the archive
Read-Only Bulletin Board.: 1/17/11, 10:28AM (Mon)

Page last modified: 2/16/11, 10:39AM 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: 11:00am-12:00pm, Tue/Thu

TA:

Ji Ryang Chung Email: jiryang(a)gmail.com Office: HRBB 502D
Phone: 862-4871
Office hours: MWF 10am-11am

Prerequisite/Restrictions:

CPSC 420, 625, or consent of instructor.

Lectures:

Tue/Thu 9:35am–10:50am, HRBB 113.

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 other public systems in the departmental lab.

Topics to be covered:

See the Weekly Schedule section for more details. The content will closely reflect a combination of Alpaydin + Mitchell.

Grading:

  1. 3 assignments (including written and programming components), 16% each = 48%
  2. 2 exams (in class), 15% each = 30%
  3. Term project 17%
  4. Class participation 5% (repeated absences in the class will weigh most heavily [in the negative direction] in the determination of this score)
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://aggiehonor.tamu.edu/

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 1/18 Introduction Alpaydin chap 1; Mitchell 1.1–1.2, 1.3–1.5     slide01.pdf
slide01b.pdf
1 1/20 Introduction Alpaydin chap 1; Mitchell 1.1–1.2, 1.3–1.5     slide01.pdf
slide01b.pdf
2 1/25 Supervised Learning (theory) Alpaydin chap 2; Mitchell 7.1–7.2, 7.4     slide02.pdf
2 1/27 Supervised Learning (theory) Alpaydin chap 2; Mitchell 7.1–7.2, 7.4     slide02.pdf
3 2/1 Multilayer perceptrons Alpaydin chap 11; Mitchell chap 4     slide03.pdf
3 2/3 Multilayer perceptrons Alpaydin chap 11; Mitchell chap 4     slide03.pdf
4 2/8 Multilayer perceptrons Alpaydin chap 11; Mitchell chap 4     slide03.pdf
4 2/10 Multilayer perceptrons Alpaydin chap 11; Mitchell chap 4 Homework 1 announced   slide03.pdf
5 2/15 Decision tree learning Alpaydin chap 9; Mitchell chap 3     slide04.pdf
5 2/17 Decision tree learning Alpaydin chap 9; Mitchell chap 3     slide04.pdf
6 2/22 Reinforcement learning Alpaydin chap 18; Mitchell chap 13     slide05.pdf
6 2/24 Reinforcement learning Alpaydin chap 18; Mitchell chap 13   Homework 1 due slide05.pdf
7 3/1 Reinforcement learning Advanced topics     slide05.pdf
7 3/3 Genetic Algorithms Mitchell chap 9     slide06.pdf
8 3/8 Exam #1        
8 3/10 Genetic Algorithms Mitchell chap 9     slide06.pdf
9 3/15 Spring break        
9 3/17 Spring break        
10 3/22 Genetic Algorithms Advanced topics     slide07.pdf
10 3/24 Genetic Algorithms, Project ideas Advanced topics     slide07.pdf
11 3/29 Dimensionality reduction Alpaydin chap 6: 6.1–3, 6.7, 6.8     slide08.pdf
11 3/31 Local models Alpaydin chap 12     slide09.pdf
12 4/5 Local models Alpaydin chap 12     slide09.pdf
12 4/7 Bayesian learning Alpaydin chap 3; Mitchell 6.5–6.9, 6.11–6.13     slide10.pdf
13 4/12 Library info session; Graphical models Library info session by Gary Wan (TAMU library); Alpaydin chap 16, Mitchell chap 6     slide10.pdf
13 4/14 Bayesian learning Mitchell chap 6     slide10.pdf
14 4/19 Kernel machines Alpaydin 13.1–13.5     slide11.pdf
14 4/21 Learning in biological vision See slide page 46 for references     slide13.pdf
15 4/26 Exam #2        
15 4/28 Learning in biological vision       slide13.pdf


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