CENG 463

Introduction to Machine Learning

An Introduction to the machine learning with examples in different application areas. Bayesian decision theory. Supervised learning techniques. Model selection. Dimensionality reduction. Clustering. Support vector machines. Graphical models. Introduction to neural networks. Reinforcement learning.

Concepts in Machine Learning
Bayesian Decision Theory
Supervised Learning Fundamentals
Linear Regression
Logistic Regression
Model Selection Procedures
Multivariate Classification, Multivariate Regression
Summary and Examination
Dimensionality Reduction and Principal Component Analysis
Linear Discriminant Functions
Neural Networks
Supervised Learning: Non-parametric approaches
Design and Analysis of Machine Learning Experiments