46838 Machine Learning for Computational Finance

46838 Machine Learning for Computational Finance

Spring Mini 4, 1999

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:

Class runs from March 8th till April 26th inclusive. No class on March 22nd (CMU Spring break).
NB: The week of April 19th, the London and Pittsburgh A classes will be taught on Wednesday, April 21st.

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:

Rosie Jones (rosie+46838 @ cs.cmu.edu), Cyert Hall, 412/268-8492
Office hours (Recitation): These sessions will be broadcast to NY, London at the times listed above.
Location: FASTLab, or by appointment
Saturday April 10th, office hours will be in NY thus there will be no London office hours on this date; alternative arrangements will be made in class.
(no office hours March 20th)

Course Secretary:

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

Administrative Help:

Pittsburgh: Norene Mears (nm10+@andrew.cmu.edu), GSIA 128, 412-268-7358
New York: Yuvelin Tejeda (tejeda+@andrew.cmu.edu), 212-584-0925
London: Mandy Hossami (Mandy.Hossami@dresdnerkb.com) 011-44-171-475-6187

Textbooks:

Machine Learning, by Tom Mitchell, McGraw Hill. Available at the CMU book store, or can be ordered online.

Grading:

Will be based on homeworks (60%) and a final (40%).
Note: you must pass the final to pass the course.

Policy on homework:

Announcements Last Updated 4/22/99

Homework Assignments:

MATLAB Tutorial

Tentative Syllabus (subject to change)

  • Lecture 1, Mar 08 1999: Course overview, introduction to Machine Learning, Concept Learning. Class-notes available as:
  • Lecture 2, March 15 1999: More on concept learning, Decision Trees
    Class-notes available as: Reading (distributed in class):
  • Mar 22 1999 NO CLASS TODAY
  • Lecture 3, Mar 29 1999 Decision Trees: complete class notes, discuss paper Inducing Stock Screening Rules for Portfolio Construction, beginning of neural networks
    Classnotes available as:
  • Lecture 4, Apr 5 1999 Neural Networks continued
    -- London will have this class on April 9th
    Readings (distributed April 5th, may arrive April 7th):
  • Lecture 5, Apr 12, 1999 Guest Lecturer James Thomas -- Genetic Algorithms for Computational Finance
    Readings (distributed April 5th, may arrive April 7th): 2 sets of class-notes available as:
  • Lecture 6, Apr 19, 1999 Instance Based Learning (locally weighted regression, k-nearest neighbor)
    --This class will be taught live in New York on Apr 19, live in London on Wednesday Apr 21 Class-notes available as:
    We will also start Hidden-markov models and EM in this class.
    (Some notes on HMM and EM, which may or may not be relevant, and which are not the ones presented in class, can be found at here)
  • Lecture 7, Apr 26, 1999