Module 06-26428 (2019)

The module will provide a solid foundation to machine learning. It will give an overview of many of the core concepts, methods, and algorithms in machine learning, covering several forms of supervised and unsupervised learning. It also introduces the basics of computational learning theory, leading up to more advanced topics like boosting and ensemble methods. The module will give the student a good understanding of how, why and when do various modern machine learning methods work. It will also give them experience of applying machine learning methods in practice, and an awareness of the issues, techniques and open problems posed by high dimensionality in machine learning.

• introduce the basic concepts and terminology of machine learning
• give an overview of the main approaches to machine learning
• show similarities and differences between different approaches
• present basic principles for the classification of approaches to machine learning
• give practical experience of applying machine learning algorithms to classification and data analysis problems

1. demonstrate a knowledge and understanding of the main approaches to machine learning
2. demonstrate the ability to apply the main approaches to unseen examples
3. demonstrate an understanding of the differences, advantages and problems of the main approaches in machine learning
4. demonstrate an understanding of the main limitations of current approaches to machine learning, and be able to discuss possible extensions to overcome these limitations
5. demonstrate a practical understanding of the use of machine learning algorithms

None

2 hrs lectures, exercise and laboratory classes per week.

Contact Hours:

23

Sessional: 1.5 hr examination (80%), continuous assessment (20%).

Supplementary (where allowed): By examination only.

1. Overview of machine learning. Basic notions, literature
2. Supervised learning
   • Generative algorithms
   • Discriminative algorithms
   • Computational learning theory basics
   • Boosting and ensemble methods
3. Unsupervised learning
   • Clustering
   • Learning for structure discovery
4. Reinforcement learning basics
5. Topics in learning from high dimensional data and large scale learning