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COMP24111 Machine Learning and Optimisation syllabus 2017-2018

COMP24111 materials

COMP24111 Machine Learning and Optimisation

Level 2
Credits: 10
Enrolled students: 194

Course leader: Ke Chen


Additional staff: view all staff

Requisites

  • Pre-Requisite (Compulsory): COMP14112
  • Pre-Requisite (Compulsory): COMP11120
  • Pre-Requisite (Compulsory): MATH10111

Additional requirements

  • Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.

    Pre-requisites

    To enrol students are required to have taken  COMP11120  and COMP14112.  Or, if you are on a Computer Science and Maths programme you must have taken MATH10111.

Assessment methods

  • 60% Written exam
  • 40% Practical skills assessment
Timetable
SemesterEventLocationDayTimeGroup
Sem 1 Lecture Simon TH A Mon 10:00 - 12:00 -
Sem 1 A Lab Toot (0 + 1) Thu 09:00 - 11:00 G
Sem 1 A Lab Toot (0 + 1) Fri 09:00 - 11:00 H
Sem 1 A Lab Toot (0 + 1) Mon 13:00 - 15:00 F
Themes to which this unit belongs
  • Learning and Search in Artificial Intelligence

Overview

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.

Aims

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.

Syllabus

  • 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

Teaching methods

Lectures

20 in total, 2 per week

2 hours of self revision

Laboratories

10 hours in total

Feedback methods

Face to face marking of all project work in lab

Study hours

  • Assessment written exam (2 hours)
  • Lectures (22 hours)
  • Practical classes & workshops (12 hours)

Employability skills

  • Analytical skills
  • 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.
  • Lab assessment
  • Examination
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 D6Understand probabilistic and generative learning models, make logistic regression and naive Bayes classifiers and interpret the results as probabilities.
  • Examination
  • Lab assessment
A1 A3 B3 C4 C5 D6Understand basic clustering algorithms and their applications to clustering analysis.
  • Examination

Reading list

TitleAuthorISBNPublisherYearCore
Introduction to machine learning (3rd edition)Alpaydin, Ethem9780262028189MIT Press2014
Pattern recognition and machine learningBishop, Christopher M.9780387310732Springer2006
Machine learning: a probabilistic perspectiveMurphy, Kevin P.9780262018029MIT Press2012

Additional notes

Course unit materials

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