CSI 5325: Introduction to Machine Learning, Spring 2008
This is a course in machine learning, which is a broad, interesting, and fast-growing field. The central problem we address in this class is how to use the computer to make models which can learn, make inferences, or improve its behavior, based on observations about the world. Further, we would like to use the learned models to make predictions about unknowns.
Machine learning is related to artificial intelligence, but also uses a lot of computer science, statistics, logic, probability, information theory, geometry, linear algebra, calculus, optimization theory, etc. It would be good to brush up on these topics if they're rusty.
Lectures are from 2:00 to 3:20 PM in Rogers 210 on Tuesdays and Thursdays.
My office hours are listed on my home page. I am glad to talk to students during and outside of office hours. If you can't come to my office hour, please email me to make an appointment at another time.
Here is a schedule of the material we will cover, which is subject to change:
|1||Jan 14-18||Assignment 1||1, 2||Introduction||Concept learning|
|2||Jan 21-25||2, 3||Concept learning||Decision trees|
|3||Jan 28-Feb 1||Assignment 2||3||Decision trees||Decision trees|
|4||Feb 4-8||4||Neural networks||Neural networks|
|5||Feb 11-15||Assignment 3||4, 5||Neural networks||Evaluating hypotheses|
|6||Feb 18-22||5, 6||Evaluating hypotheses||Bayesian learning|
|7||Feb 25-29||6||Bayesian learning||Bayesian learning|
|8||Mar 3-7||7||Learning theory||MIDTERM|
|9||Mar 10-14||Spring break|
|10||Mar 10-14||Assignment 4||7||Learning theory||Learning theory|
|11||Mar 24-28||8||Instance-based learning||Instance-based learning|
|12||Mar 31-Apr 4||Assignment 5||Burges paper; Alpaydin chapter; Schölkopf chapter||Instance-based learning||Support vector machines|
|13||Apr 7-11||Support vector machines||Support vector machines|
|14||Apr 14-18||Duda et al. chapter 10||Unsupervised learning||Diadeloso|
|15||Apr 21-25||Unsupervised learning||Unsupervised learning|
|16||Apr 28-May 2||Freund and Schapire||Boosting||Project presentation|
The final exam will tentatively be on Monday, May 12 at 9 AM. As of writing this, the final exam schedule is not yet definite. The latest university finals information is available here.
Paper reading/presentation component
As a part of the assignments, each student will present one paper on a topic which we are studying. Here is the list of topics, papers and people who are signed up.
Everyone in the class is expected to read the paper that is presented so we may have a fruitful discussion.
Textbooks & resources
We will also be reading from some papers in the research literature.
Optional texts (I may draw some material from these):
- Pattern recognition and machine learning by Christopher Bishop.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
- Introduction to Machine Learning by Ethem Alpaydin.
Further online resources:
- We will use Blackboard as a class discussion board.
- Andrew Moore has a number of nice tutorials on topics related to machine learning
- Tommi Jaakkola and MIT OpenCourseWare have provided a machine learning course with course materials available online
- KDD dataset repository -- has many popular machine-learning datasets
- Matlab tutorial
- LaTeX introduction, another LaTeX introduction, LaTeX reference
- Matrix reference manual
Grades will be assigned based on this breakdown:
- homework/projects: 50%
- midterm exam: 20%
- final exam: 30%
Here is a tentative grading scale:
A: 90-100, B+: 88-89, B: 80-87, C+: 78-79, C: 70-77, D: 60-69, F: 0-59
Some projects may be worth more than others. Exams are closed-book. The final will be comprehensive.
- Check this website every day for updates and announcements. We only meet three times a week, but I may post updates at any time. It is your responsibility to follow these updates by reading this website.
- All work in this course is strictly individual, unless the instructor explicitly states otherwise. While discussion of course material is encouraged, collaboration on any work for the course is not allowed. Collaboration includes (but is not limited to) discussing with anyone other than the professor any material that is specific to completing an assignment. You are encouraged to discuss the course material with the professor, preferably in office hours, and also by email.
- Baylor policy requires 75% class attendance from each student. Even "excused" absences are included in the overall absent count. If a student attends less than 75% of the classes, he or she will automatically fail the course.
- Projects which are late are not accepted without prior arrangement. Exams may be made up with prior arrangement (made at least one class before to the exam) or due to illness, with a note from a health care professional.
- Bring any grading correction requests to my attention within 2 weeks of receiving the grade or before the end of the semester, whichever comes first. After that, I will not adjust your grade. If you find any mistake in grading, please let me know.
I take academic honesty very seriously.
Many studies, including one by Sheilah Maramark and Mindi Barth Maline have suggested that "some students cheat because of ignorance, uncertainty, or confusion regarding what behaviors constitute dishonesty" (Maramark and Maline, Issues in Education: Academic Dishonesty Among College Students, U.S. Department of Education, Office of Research, August 1993, page 5). In an effort to reduce misunderstandings in this course, a minimal list of activities that will be considered cheating have been listed below.
- Copying another student's work. Simply looking over someone else's source code is copying.
- Providing your work for another student to copy.
- Collaboration on any assignment, unless the work is explicitly given as collaborative work.
- Using notes or books during any exam.
- Giving another student answers during an exam.
- Reviewing a stolen copy of an exam.
- Studying tests or using assignments from previous semesters.
- Providing someone with tests or assignments from previous semesters.
- Taking an exam for someone else.
- Turning in someone else's work as your own work.
- Studying a copy of an exam prior to taking a make-up exam.
- Providing a copy of an exam to someone who is going to take a make-up exam.
- Giving test questions to students in another class.
- Reviewing previous copies of the instructor's tests without permission from the instructor.