COMP24111 Machine Learning and Optimisation syllabus 2017-2018
Machine learning is concerned with creating learning models that allow a computer to exhibit behaviour that would normally require a human. Typical applications might be computer vision, speech recognition, machine translation, natural language processing, medical diagnosis, intelligent robots, and so on. The learning models come in various forms, e.g. parametric and non-parametric and probability distributions. The emphasis is on constructing these models automatically from data---for example making a face recogniser from a data file of facial images. This course will introduce you to the concepts behind various Machine Learning techniques, including how they work, and use existing software packages to illustrate how they are used on data. The course has a fairly mathematical content although it is intended to be self-contained.
To introduce methods for learning from data, and provide the necessary mathematical background to enable students to understand how the methods work, how to evaluate the performance a machine learning system and how to get the best performance from them. This course covers basics of both supervised and unsupervised learning paradigms and is pitched towards any student with a mathematical or scientific background who is interested in adaptive techniques for learning from data as well as data analysis and modelling.
- Machine Learning Basics
- K Nearest Neighbour Classifier
- Linear Classification/Regression
- Logistic Regression
- Support Vector Machine
- Deep Learning Models
- Generative Models and Naïve Bayes
- Basics of Clustering Analysis
- K-mean Clustering
- Hierarchical and Ensemble Clustering
- Cluster Validation
20 in total, 2 per week
2 hours of self revision
10 hours in total
Face to face marking of all project work in lab
- Assessment written exam (2 hours)
- Lectures (22 hours)
- Practical classes & workshops (12 hours)
- Analytical skills
- Project management
- Problem solving
- Written communication
On successful completion of this unit, a student will be able to:
Learning outcomes are detailed on the COMP24111 course unit syllabus page on the School of Computer Science's website for current students.
|Introduction to machine learning||Alpaydin, Ethem.||9780262325745||The MIT Press||2014|
|Pattern recognition and machine learning||Bishop, Christopher M., author.||9780387310732||Springer||2006|
|Machine learning : a probabilistic perspective||Murphy, Kevin P., 1970-||9780262018029||MIT Press||©2012.|
|Machine learning||Mitchell, Tom M. (Tom Michael), 1951-||0070428077||WCB/McGraw-Hill||1997.|
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.