46-838 Machine Learning for Computational Finance

Spring Mini 4, 1998

Prof. Roni Rosenfeld School of Computer Science, Carnegie Mellon University

The purpose of this course is to give a broad introduction to the techniques of machine learning, and to place those techniques within the context of computational finance. Machine learning is concerned with building computer programs that learn and improve with experience. The class will start out with an introduction of the underlying philosophy and methodology of machine learning, and then move on to hands on application of such techniques as neural nets and decision trees to real financial datasets.

Time and Place: Wednesdays, 5:30PM--8:30PM, FastLab, GSIA.

Instructor:

Roni Rosenfeld (roni@cs.cmu.edu), Wean Hall 4109, 412/268-7678. Fax: 412/268-5576.

Office hours: Thursday, 11AM-noon, or by appointment (set up via email).

Teaching Assistant:

James Thomas (jthomas@cs.cmu.edu), Wean Hall 7125, 412/268-8734

Office hours: 2-4 Sundays, FASTLab, or by appointment

Course Secretary:

Dorothy Zaborowski (daz+@cs.cmu.edu), Wean Hall 4116. 412/268-3779.

Administrative Help:

Pittsburgh: Elizabeth Kelly (eb1k+@andrew.cmu.edu), GSIA 129, 412-268-7358

New York: Yuvelin Tejeda (tejeda+@andrew.cmu.edu), 212-603-3899

Textbooks:

Machine Learning, by Tom Mitchell, McGraw Hill. Available at the CMU book store.

Grading:

Will be based on homeworks (60%) and a final (40%).

Note: you must pass the final to pass the course.

Policy on homework:

Unless otherwise stated, homework is due at 5:30 the Wednesday after it is assigned. For the next 24 hours, the homework will be worth half credit, unless other arrangements were made in advance. After that, it will be worth zero credit, unless other arrangements were made in advance, but still needs to be done. To be considered, a potential hardship should be brought to the instructor's attention as soon as it becomes known.
Assignments should be turned in to the TA. For Pittsburgh students, this means turning them in during class. For New York, this means giving them to Yuvelin on Wednesday, who will fedex them to 7125 Wean. For assignments that must be handed in electronically, instructions will be posted with the assignment.
Unless otherwise stated, all homework assignments are to be done completely on your own, with no communication with anyone except the TA or the instructor. Communication about the homework continues to 1be prohibited until turning them in is worth zero credit (usually Thursday 5:30PM). This policy will be strictly enforced.

Announcements: Last updated 2/16

Homework Assignments:

Assignment #1 (Due 6:00 PM, March 31, 1998) Solutions *
Assignment #2 (Due 5:30 PM, April 8, 1998) *Solutions temporarily withdrawn for correction*
Assignment #3 (Due 5:30 PM, April 15, 1998) Solutions *
Assignment #4 (Due 5:30 PM, April 21, 1998) Solutions *
Assignment #5 (Due 5:30 PM, April 28, 1998) Solutions *

Tentative Syllabus (subject to change)
Lecture 1, Mar 11. Final Exam Review.
Lecture 2, Mar 18. Bayesian Learning. (Chapter 7) 1 to a page postscript * 4 to a page postscript * adobe .pdf
Lecture 3, Apr 1. Bayesian Learning, continued. (Chapter 7)
Lecture 4, Apr 8. Instance Based Learning. (Chapter 8) 1 to a page postscript * 4 to a page postscript * adobe .pdf
Lecture 9, Apr 15. Genetic Algorithms. (Chapter 9) 1 to a page postscript * 4 to a page postscript * adobe .pdf
Lecture 10, Apr 22. Reinforcement Learning. (Chapter 9) 1 to a page postscript * 4 to a page postscript * adobe .pdf

46-838 Machine Learning for Computational Finance

Spring Mini 4, 1998

Tentative Syllabus (subject to change)