Module 06-20236 (2014)
Machine Learning (Extended)
|Ata Kaban||Semester 1||10 credits|
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.
The aims of this module are to:
- 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.
- Develop skills of literature surveying and critical thinking in an area of machine learning.
On successful completion of this module, the student should be able to:
- Demonstrate a knowledge and understanding of the main approaches to machine learning
- Demonstrate the ability to apply the main approaches to unseen examples
- Demonstrate an understanding of the differences, advantages and problems of the main approaches in machine learning
- Demonstrate an understanding of the main limitations of current approaches to machine learning, and be able to discuss possible extensions to overcome these limitations
- Demonstrate a practical understanding of the use of machine learning algorithms
- survey and discuss the research literature in one subfield of machine learning
Cannot be taken with
- 06-26428 - Machine Learning
Sessional: 1.5 hr examination (60%), continuous assessment (40%).
Supplementary (where allowed): By examination only.
- Overview of machine learning. Basic notions, literature
- 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
Programmes containing this module
- MEng Computer Science/Software Engineering 
- MEng Computer Science/Software Engineering with an Industrial Year 
- MSc Advanced Computer Science 
- MSc Computer Science 
- MSc Computer Security 
- MSc Human-Computer Interaction 
- MSc Robotics 
- MSci Computer Science 
- MSci Computer Science with an Industrial Year 
- MSci Computer Science with Study Abroad