CS 446 - Machine Learning and Pattern Recognition

• Time: 12:30 PM--1:45 PM, Wed/Fri
• Location: 1105 Siebel Center
• Instructor: Prof. Gerald DeJong Office hours: 3320 SC, Thur @ 3
• TA: Li-Lun Wang Office hours: 3332 SC, Mon @ 11am
• Textbook: Tom M. Michell. Machine Learning. McGraw-Hill.
• Lecture Slides:
  • Introduction 1: ppt
  • Introduction 2: ppt
  • Introduction 3: ppt
  • Desicion Tree 1: ppt
  • Desicion Tree 2: ppt
  • Desicion Tree 3: ppt
  • Computational Learning Theory 1: ppt
  • Computational Learning Theory 2: ppt
  • Support Vector Machines: pdf
    • Additional Resource on SVM and other kernel methods
  • Kernel Perceptron: ppt
  • Computational Learning Theory 3: ppt
  • Computational Learning Theory 4: ppt
  • Computational Learning Theory 5: ppt
  • Computational Learning Theory 6: ppt
  • Bayesian Learning 1: ppt
  • Bayesian Learning 2: ppt
  • Bayesian Learning 3: ppt
  • Bayesian Learning 4: ppt
  • Bayesian Learning 5: ppt
  • Bayesian Learning 6: ppt
  • EM: ppt
  • Markov Random Field / Information Theory: ppt
  • Hierarchical Bayesian Modeling / Dirichlet Process: ppt
  • Discriminative Classifiers / Logistic Regression / Bayes Optimal: ppt
  • Ensemble Methods: ppt
  • Misc 1: ppt
  • Misc 2: ppt
  • Misc 3: ppt
• Assignments:
  • Homework 1, due Wednesday September 20th.
    • Data set: Train, Test
    • FAQ Updated Monday September 18th.
  • Homework 2, due Wednesday December 6th. (Updated Saturday November 18th in blue and green)
    • Training subsets of various sizes:
      • 5
      • 10
      • 15
      • 20
      • 25
      • 30
      • 35
      • 40
      • 45
      • 50
      • 55
      • 60
      • 65
      • 70
      • 75
      • 80
      • 85
      • 90
      • 95
      • 100
    • Testing set
    • Whole data set
    • FAQ Updated Wednesday December 6th.