CmpE 545 Artificial Neural Networks

Spring Semester

Ethem ALPAYDIN


Textbook

Introduction to Machine Learning, 3e, The MIT Press, 2014


Topics

  1. Chap 10. Linear Discrimination
  2. Chap 11. Multilayer Perceptrons
  3. Chap 12. Local Models: Radial Basis Functions and Mixture of Experts
  4. Chap 13 Kernel Machines
  5. Chap 14 Bayesian Estimation
  6. Chap 16 Graphical Models
  7. Chap 17. Combining Multiple Learners
  8. Chap 18. Reinforcement Learning
  9. Chap 19. Design and Analysis of Machine Learning Algorithms
  10. Advanced topics on Bayesian Networks, Kernel methods, and Nonlinear dimensionality reduction

Goals

An artificial neural network is an interconnected set of simple concurrently operating processing units. This course follows CmpE 544 Pattern Recognition and dicusses novel methods using such networks in a comparitive manner with the classical approaches discussed in CmpE 544.


Prerequisite by Topic

CmpE 544, Undergraduate level calculus, probability theory. Prior experience in a high-level programming language.


Computer Usage

Almost all homeworks require computer simulations. I recommend using Matlab.


Grading

1 Project 0.30

1 Final 0.30

Homeworks 0.40


Last modified on Feb 20,2015.