## CmpE 545 Artificial Neural Networks

### Spring Semester

### Ethem ALPAYDIN

### Textbook

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

### Topics

- Chap 10. Linear
Discrimination
- Chap 11. Multilayer Perceptrons
- Chap 12. Local Models:
Radial Basis Functions and Mixture of Experts
- Chap 13 Kernel Machines
- Chap 14 Bayesian Estimation
- Chap 16 Graphical Models
- Chap 17. Combining Multiple
Learners
- Chap 18. Reinforcement
Learning
- Chap 19. Design and
Analysis of Machine Learning Algorithms
- 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.*