This is an archived syllabus from 2013-2014
COMP24111 Machine Learning and Optimisation syllabus 2013-2014
COMP24111 Machine Learning and Optimisation
Level 2
Credits: 10
Enrolled students: 91
Course leader: Gavin Brown
Additional staff: view all staff
Requisites
- Pre-Requisite (Compulsory): COMP14112
Additional requirements
Pre-requisites
COMP11120 or equivalent e.g. (MATH10662 and MATH10672 or MATH10111 and MATH10131 and MATH10212)
Assessment methods
- 50% Written exam
- 50% Practical skills assessment
| Semester | Event | Location | Day | Time | Group |
|---|---|---|---|---|---|
| Sem 1 A | Lab | G23 | Fri | 09:00 - 09:00 | H |
| Sem 1 A | Lab | LF31 | Thu | 09:00 - 09:00 | G |
| Sem 1 w1 | Lecture | 1.4 | Tue | 13:00 - 13:00 | - |
| Sem 1 w2-5 | Lecture | IT407 | Tue | 13:00 - 13:00 | - |
| Sem 1 w7-12 | Lecture | Uni Place 1.218 | Tue | 13:00 - 13:00 | - |
- Learning and Search in Artificial Intelligence
Overview
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.
Aims
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.
Syllabus
- Introduction to Machine Learning
- K Nearest Neighbour Classifier
- Decision Trees
- Model Selection and Empirical Methodologies
- Linear Classifiers: Perceptron and SVM
- Na?ve Bayes Classifier
- Basics of Clustering Analysis
- K-mean Clustering Algorithms
- Hierarchical Clustering Algorithms
Teaching methods
Lectures
22 in total, 2 per week
Laboratories
10 hours in total, 5 2-hour sessions, partly credited to COMP20910/COMP20920
Feedback methods
Face to face marking of all project work.Study hours
- Assessment written exam (2 hours)
- Lectures (22 hours)
- Practical classes & workshops (12 hours)
Employability skills
- Analytical skills
- Group/team working
- Project management
- Problem solving
- Written communication
Learning outcomes
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.
Reading list
No reading list found for COMP24111.
Additional notes
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.