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COMP24111 Machine Learning and Optimisation syllabus 2019-2020

COMP24111 materials

COMP24111 Machine Learning and Optimisation

Level 2
Credits: 10
Enrolled students: 230

Course leader: Tingting Mu


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

  • 70% Written exam
  • 30% Practical skills assessment
Timetable
SemesterEventLocationDayTimeGroup
Sem 1 Lecture Roscoe TH B Tue 16:00 - 18:00 -
Sem 1 A Lab 2.25 (A+B) Thu 09:00 - 11:00 H
Sem 1 A Lab LF31 Fri 12:00 - 14:00 F
Sem 1 A Lab G23 Fri 16:00 - 18:00 G
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 include computer vision, speech synthesis and recognition, natural language processing, medical diagnostics, intelligent robots, vehicle autonomy, material sciences, etc. The learning models come in various forms, e.g., parametric and non-parametric, deterministic and probabilistic. The emphasis is on constructing these models automatically from data; for example, making a face recogniser from data files of facial images. This course will provide an introduction to the necessary mathematical background and foundational principles and mechanisms to key techniques in machine learning, as well as their implementation, application and evaluation. At the end of this course, students will be expected to have a theoretical understanding of key machine learning topics and be equipped with practical skills on their usage. The course has a fairly mathematical and algorithmic content and is intended to be self-contained. The lab work is based on Python.

Aims

Our aims include the introduction of the essential concepts behind key machine learning techniques, the methodologies for building machine learning systems, the approaches for learning from data, and the experimental methods on how to evaluate the performance of a learning system and get the best performance from it. Also, we aim to provide the necessary mathematical background required to understand how the methods work. This course covers basics on both supervised and unsupervised learning paradigms and is pitched towards students with scientific/mathematical background interested in adaptive techniques for learning from data as well as data analysis and modelling.

Specifically, the course will cover the following. A general introduction to key concepts in machine learning and the development of the field. Essential knowledge on how to build a supervised machine learning system, including classification and regression, with respect to model architecture (e.g., instance-based model, linear model, linear basis function model, kernel methods, single- and multi-layer perceptrons, etc.), loss functions (e.g., sum of squares error, regularisation, cross-entropy, etc.), and optimisation approaches for training (e.g., basic optimality conditions, (stochastic) gradient descent, etc.). Basic knowledge on parametric, non-parametric, deterministic and probabilistic models. Essential knowledge on clustering analysis. Essentials on how to measure performance of a machine learning system for classification, regression and clustering. Basics on bias and variance issues, over-fitting and under-fitting. Essential knowledge and practical skills on how to perform machine learning experiments, data usage for model training, model selection and model testing.

Syllabus

  • Machine Learning Basics
  • k-Nearest Neighbours
  • Machine Learning Experiments
  • Machine Learning Models
  • Loss Functions
  • Training and Optimisation
  • Artificial Neural Networks
  • Support Vector Machines
  • Clustering Analysis
  • Deep Learning Models

Teaching methods

Lectures & Revision Classes:

22 hours in total, 2-hour session per week

Laboratories:

10 hours in total, 5 x 2-hour sessions, partly credited to COMP20910/COMP20920

Feedback methods:

Face to face marking of all project work in the lab

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

On successful completion of this unit, a student will be able to:

  • Understand essential concepts in machine learning
  • Understand the fundamental principals of the studied learning techniques and essential elements for building a machine learning system
  • Understand the advantages and disadvantages of the studied supervised learning methods, and decide which is appropriate for a particular application
  • Understand basic clustering algorithms studied in the course and their applications to clustering analysis
  • Understand the differences between parametric and non-parametric, between deterministic and probabilistic models, and interpret the results
  • Understand how to use data, design experiments for learning, model selection and evaluation, and make observations from results
  • Understand general factors that affect the performance of a machine learning system, and be able to use these to analyse and learn from data
     

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