Enrolment on this course unit is limited to 150 students.
COMP24111 Machine Learning and Optimisation syllabus 2015-2016
Machine learning is concerned with creating mathematical "data structures" that allow a computer to exhibit behaviour that would normally require a human. Typical applications might be spam filtering, speech recognition, medical diagnosis, or weather prediction. The data structures we use (known as "models") come in various forms, e.g. trees, graphs, algebraic equations, probability distributions. The emphasis is on constructing these models automatically from data---for example making a weather predictor from a datafile of historical weather patterns. 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 extracting rules or learning from data, and provide the necessary mathematical background to enable students to understand how the methods work 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.
- Introduction to Machine Learning
- K Nearest Neighbour Classifier
- Decision Trees
- Model Selection and Empirical Methodologies
- Linear Classifiers: Perceptron and SVM
- Naive Bayes Classifier
- Basics of Clustering Analysis
- K-mean Clustering Algorithms
- Hierarchical Clustering Algorithms
22 in total, 2 per week
10 hours in total, 5 2-hour sessions, partly credited to COMP20910/COMP20920
Feedback methodsFace to face marking of all project work.
- Assessment written exam (2 hours)
- Lectures (22 hours)
- Practical classes & workshops (12 hours)
- Analytical skills
- Group/team working
- Project management
- Problem solving
- Written communication
|Programme outcome||Unit learning outcomes||Assessment|
|A1 A3 B3 D6||Evaluate whether a learning system is appropriate for a particular problem.|
|A1 A3 B3 D6||Understand how to use data for learning, model selection, and testing.|
|A1 A3 B3 D6||Understand generally the relationship between model complexity and model performance, and be able to use this to design a strategy to improve an existing system.|
|A1 A3 B3 C4 D6||Understand the advantages and disadvantages of the learning systems studied in the course, and decide which is appropriate for a particular application.|
|A1 A3 B3 C4 C5 D6||Make a naive Bayes classifier and interprete the results as probabilities.|
|A1 A3 B3 C4 C5 D6||Be able to apply clustering algorithms to simple data sets for clustering analysis.|
|Introduction to machine learning (3rd edition)||Alpaydin, Ethem||9780262028189||MIT Press||2014||✔|
|Artificial Intelligence: a modern approach (2nd edition)||Russell, S. and P. Norvig||0130803022||Prentice Hall||2003||✖|
|Pattern recognition (4th edition)||Theodoridis, Sergios and Konstantonis Koutroumbas||9781597492720||Elsevier||2009||✖|
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.