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This is an archived syllabus from 2014-2015

COMP24111 Machine Learning and Optimisation syllabus 2014-2015

COMP24111 Machine Learning and Optimisation

Level 2
Credits: 10
Enrolled students: 123

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
Timetable
SemesterEventLocationDayTimeGroup
Sem 1 w1-5 Lecture IT407 Tue 13:00 - 15:00 -
Sem 1 A Lab G23 Fri 09:00 - 11:00 H
Sem 1 A Lab LF31 Thu 09:00 - 11:00 F
Sem 1 w7-12 Lecture Zochonis TH A Tue 13:00 - 15:00 -
Themes to which this unit belongs
  • 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
  • Naive 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

Programme outcomeUnit learning outcomesAssessment
A1 A3 B3 D6Evaluate whether a learning system is appropriate for a particular problem.
  • Examination
A1 A3 B3 D6Understand how to use data for learning, model selection, and testing.
  • Examination
  • Lab assessment
A1 A3 B3 D6Understand generally the relationship between model complexity and model performance, and be able to use this to design a strategy to improve an existing system.
  • Examination
  • Lab assessment
A1 A3 B3 C4 D6Understand the advantages and disadvantages of the learning systems studied in the course, and decide which is appropriate for a particular application.
  • Examination
A1 A3 B3 C4 C5 D6Make a naive Bayes classifier and interprete the results as probabilities.
  • Examination
  • Lab assessment
A1 A3 B3 C4 C5 D6Be able to apply clustering algorithms to simple data sets for clustering analysis.
  • Examination
  • Lab assessment

Reading list

TitleAuthorISBNPublisherYearCore
Introduction to machine learning (3rd edition)Alpaydin, Ethem9780262028189MIT Press2014
Artificial Intelligence: a modern approach (2nd edition)Russell, S. and P. Norvig0130803022Prentice Hall2003
Pattern recognition (4th edition)Theodoridis, Sergios and Konstantonis Koutroumbas9781597492720Elsevier2009

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.