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.
| Topics |
| 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
|
| Clustering |
| Linear Discriminant Functions |
| Neural Networks |
| Supervised Learning: Non-parametric approaches |
|
Design and Analysis of Machine Learning Experiments
|
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