Machine Learning: Foundations (2012/13)

Tentative Class schedule:

  1. Introduction
  2. Bayesian Inference [slides]
  3. PAC model and Occam Razor
  4. Online Learning: Mistake Bound, Winnow, Perceptron.
  5. Regret Minimization
  6. Boosting and Margin
  7. Nearest Neighbor
  8. VC dimension I - definition and impossibility result [ppt]
  9. VC dimension II - sample bound (Rademacher complexity)
  10. Convex Programming and Support Vector Machine [Andrew Ng class notes]
  11. Kernels, SVM and SMO algorithm
  12. Regression Models
  13. Model Selection

 

Homework

submission guidelines.

Homework 1

Homework 2

Homework 3

Homework 4

 

Data Sets

Iris

mnist

ISOLET

Glass

 

BOOK

Foundations of machine learning Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar; MIT Press, 2012

 

SCRIBE

Scribe notes: Each student will write a scribe note for a lecture (template [pdf,tex] explanation on Latex [pdf,tex])

Scribe list

FINAL PROJECT

STUDENT PROJECTS

Courses on Machine Learning Elsewhere:

PPTX Presentations (students)

Nearest Neighbor

Bayes Inference

SVM

VC dimension

AdaBoost

PAC