Machine Learning (20cr)
| Code | School | Level | Credits | Semesters |
| COMP4139 | Computer Science | 4 | 20 | Autumn UK |
- Code
- COMP4139
- School
- Computer Science
- Level
- 4
- Credits
- 20
- Semesters
- Autumn UK
Summary
Providing you with an introduction to machine learning, pattern recognition, and data mining techniques. This module will enable you to consider both systems which are able to develop their own rules from trial-and-error experience to solve problems, as well as systems that find patterns in data without any supervision. In the latter case, data mining techniques will make generation of new knowledge possible, including very big data sets. This is now fashionably termed 'big data' science. You'll cover a range of topics including: machine learning foundations; pattern recognition foundations; artificial neural networks; deep learning; applications of machine learning; data mining techniques and evaluating hypotheses. You will also have the opportunity to work on real-world datasets and gain experience in technical paper writing in the format of conference publications.
You'll spend around six hours each week in lectures and computer classes for this module.
Target Students
Available to Level 4 students in the School of Computer Science. This module requires solid high-level computer programming skills (e.g. Python) and mathematical skills (e.g. linear algebra, differentiation, probability). This module is part of the Artificial Intelligence, Modelling and Optimisation theme in the School of Computer Science.
Assessment
- 30% Coursework 1: Group programming assignments include developing machine learning solutions for real world applications and scientific paper writing.
- 70% Exam 1 (2-hour): ExamSys in person
Assessed by end of autumn semester
Educational Aims
To introduce the principles, techniques and applications of machine learning and pattern recognition;To enable students to appreciate some of the most widely used machine learning and pattern recognition algorithms and applications, as well as data mining techniques and their applications;To enable the students to understand and be able to put into practice a variety of machine learning and pattern recognition algorithms, as well as data mining techniques;To enable students to apply data mining techniques on real data sets, some of which can be described as big data sets;To allow students to appreciate the potential and limitation of big data.Learning Outcomes
Knowledge and Understanding
- Understanding the capabilities, strengths and limitations of machine learning paradigms(A3)
- An appreciation of learning systems and learning algorithms (A4)
Intellectual Skills
- The ability to understand complex ideas and relate them to specific situations (B4)
- The ability to identify both capabilities and limitations of a machine learning or pattern recognition method (B4)
Professional Skills
- The ability to implement selected machine learning operations including learning algorithms and apply them in real world applications (C1)
- The ability to evaluate available machine learning models and learning algorithms and select those appropriate to a given task (C3)
Transferable Skills
- The ability to address real problems and assess the value of their proposed solutions (D1)
- The ability to describe the method, evaluation process and result analysis in technical expressions in the form of scientific papers (D4)