Overview

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

What you'll learn

• Introduction to the fundamental principles, techniques, and applications of Machine Learning.
• Principles, design and implementation of learning programs which improve their performance on some set of tasks by experience.
• A broad understanding of machine learning algorithms and their use in data-driven knowledge discovery.
• Understanding of the current state of the art in machine learning to conduct original research in machine learning.

Course Content

1.Introduction in Machine Learning. Statistical Foundations.

2. Decision Tree learning.

3. Artificial Neural Networks.

4. Support Vector Machines.

5. Bayesian Learning.

6. Instance based learning.

7. Unsupervised learning.

8. Reinforcement Learning.