CS534: Machine Learning--Syllabus

Personnel
    Instructor:
       Tom Dietterich, Dearborn 221C, 737-5559, tgd@cs.orst.edu
       Office Hours: Thursday 9:00-10:00am
    Grader: Charles Parker

Meeting Times
    MWF 9:00-10:00am Batchellor 250

Text    
    Pattern Classification by Duda, Hart, and Stork.
    
Goals
    When you have completed this course, you should be able to apply
    machine learning algorithms to solve machine learning algorithms
    for both iid and sequential data problems of moderate complexity.
    You should also be able to read current research papers in machine
    learning and understand the issues raised by current research in
    supervised learning.

Prerequisites
    Knowledge of machine learning algorithms from CS531 (Bayesian
    Networks (including the EM algorithm for learning with hidden
    variables); Decision trees).  Basic knowledge of data structures,
    search algorithms (gradient descent, depth-first search, greedy
    algorithms), calculus, probability.

Grading
    Homework   50%
    MidTerm    20%
    Final      30%

    Written Homework and Programs are due at the beginning of class.

    Each student is responsible for his/her own work.  The standard
    departmental rules for academic dishonesty apply to all
    assignments in this course.  Collaboration on homeworks and
    programs should be limited only to answering questions that can be
    asked and answered without using any written medium (e.g., no
    pencils, instant messages, or email).

Turning In Programming Assignments
    You will turn in your solutions to programming problems both
    electronically and as a hardcopy in class.  
    We are using the ENGR
    homework system for turning in assignments electronically.

PREDICTED COURSE SCHEDULE

       INTRODUCTION: Linear Threshold Classifiers (part1)
Mar 28 Introduction
    30 Space of Algorithms
Apr  1 Perceptrons
     4 Logistic Regression

       THE TOP 5 ALGORITHMS
     6 Linear Discriminant Analysis
     8 Off-The-Shelf Learning Algorithms
    11 Decision Trees (part2)
    13 Decision Trees (continued); Nearest Neighbor (part3)
    15 Nearest Neighbor; Neural networks (part4)
    18 Neural networks; 
    20 Support Vector Machines (part5)
    23 Naive Bayes (part6)

       LEARNING THEORY
    25 PAC Learning Theory (part7)
    27 PAC Learning Theory (continued)
    29 Bayesian Learning Theory (part8), Bias/Variance Theory (part9)

May  2 Bias/Variance Theory and Ensemble Methods (part9)
     4 Bias/Variance Theory and Ensemble Methods (continued)

       OVERFITTING AVOIDANCE
     6 Penalty Methods for Preventing Overfitting (part10)

     9 MIDTERM EXAM

    11 Hold-Out and Cross-validation Methods (part10)
       Hold-Out.  Pessimistic pruning.
    13 Penalty methods for Neural Nets and SVMs
    16 Hold-Out methods for trees, networks, nearest neighbor, SVMs (part11)

       SEQUENTIAL SUPERVISED LEARNING
    18 Introduction; Hidden Markov Models (part12)
    20 Conditional Random Fields (part12)
    23 Discriminative Methods (part12)
    25 Research Issues

       METHODOLOGY
    27 Evaluating and Comparing Classifiers (part13)

    30 Memorial Day: No Class
June 1 Evaluation continued (part13)
     3 Course Summary

Jun  6 18:00 Final Exam