cse 4/574





  • Final grades are now posted here.
  • A lecture schedule is available here.
  • CSE 4/574 is now online at UBlearns.
  • Course summary

    Course announcement: Please see the course posting/announcement for an overview of the course content.
    Course description: Teaching computer programs to improve their performance through guided training and unguided experience. Symbolic and numerical approaches. Concept learning, decision trees, neural nets, latent variable models, probabilistic inference, time series models, Bayesian learning, sampling methods, computational learning theory, support vector machines.
    Course homepage:http://www.cse.buffalo.edu/faculty/mbeal/cse574
    Location / Time:NSC 210   5-6.20pm   T R   (schedule)
    (first lecture Aug 31, last lecture Dec 9, no lecture Sep 16 & Nov 25)
    Instructor:Prof. Matthew J. Beal
    Contact:Office: 210 Bell Hall, Phone: (716) 645 3180 x154, Email: mbeal [at] cse.buffalo.edu
    TA:Harish Srinivasan
    Office hours:
    (subject to change)
    Matthew Beal: W: 11-12, 5-6, at other times by appointment   (in Bell 210)
    Harish Srinivasan: M, F: 5-6   (in CEDAR)
     Section 1: M 9-9.50am
    112 Baldy
    Section 2: F 4-4.50pm
    213 Obrian
    CSE 474398076001067
    CSE 574161162405087
    Prerequisites: 1. CSE 250 and any of EAS 305, STA 401, or permission of instructor.
    2. A basic background in statistics, calculus and linear algebra.
    3. Basic knowledge of probability will be assumed.
    (You should check that you are familiar with the material in the following crib sheet [pdf][ps].)
    Computing: Some examples and homework will require Matlab, a straightforward but powerful programming language. So, you must either know Matlab or Octave, or be taking a course in either, or be willing to learn Matlab (we will make use of this primer [pdf]).
    Required texts: T. M. Mitchell (1997) Machine Learning, McGraw Hill.
    D. J. C. MacKay (2003) Information Theory, Inference, and Learning Algorithms, Cambridge University Press (I highly recommend this book; it is also very cheap and available free for online viewing).
    Optional text: T. Hastie, R. Tibshirani, J. Friedman (2003) The Elements of Statistical Learning, Springer.
    (subject to change)
    35% Homework Assignments (best 5 out of 6, worth 7% each)
    15% Mid-Term Exam
    20% Projects (10% each)
    30% Final Exam (will also include topics covered before the Mid-Term Exam)
    Academic Honesty: Group study and discussion are encouraged, but project and homework assignments must be your own work. For coding assignments, if you use a piece of code which you borrowed from elsewhere and therefore did not write yourself, make sure you comment it to show this.
    Zero tolerance on plagiarism/cheating: if the TA suspects misconduct then you are on shaky ground and if found to be true you will be failed. Consult the University Code of Conduct for details on consequences of academic misconduct, and see also Prof. Shapiro's page on Academic Integrity of the CSE department: http://www.cse.buffalo.edu/~shapiro/Courses/integrity.html.
    Handing in work: Homeworks and Projects are due at the beginning of the lecture of the due date.
    Late homeworks will receive a 10% penalty immediately, and a further 10% for each weekday late, regardless of excuse. No credit will be awarded once the assignments are discussed in the recitations or lectures after the due date.