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.

Course Objectives

1. To form background related to the machine learning

2. To provide students with the ability of analyzing the machine learning problems and recognizing appropriate solution techniques

3. To develop the ability to implement proper solution algorithms to the problems given

Recommended or Required Reading

Ethem Alpaydın, Introduction to Machine Learning (2nd Edition), MIT Press, 2010. ,Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006. ,Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons, 2001. ,Tom Mitchell, Machine Learning, McGraw-Hill, 1997.

Learning Outcomes

1. To be able to describe the fundamental concepts in machine learning

2. To be able to analyze the machine learning problems and identify appropriate solution techniques

3. To be able to apply proper algorithms using proper tools to solve the encountered machine learning problems

4. To be able to interpret the output of the implementations from algorithmic point of view

Grading

Midterm 30%

Homework 30%

Final 40%