COMP24112
Machine Learning
10
Semester 2
Undergraduate
Level 2
Tingting Mu
Unit coordinator
Department of Computer Science
1
FHEQ level (Framework for Higher Education Qualifications) ' Middle part of Bachelors '
European Credit Transfer & Accumulation System Rating : 5.0
<div> The world is filling up with data - billions of images online, billions of supermarket transactions, billions of events pouring out of our everyday lives. Machine Learning is about designing algorithms capable of automatically learning patterns from this supplied data. Examples of this are in online shopping like Amazon.com - which learns what products you like to buy, or in spam detection systems, which learn what spam looks like as you tag it in your spam folder.</div><div> </div><div> In this course unit we will introduce you to the basics of these algorithms, implementing a basic spam filter and a handwriting recognition engine.</div>
<div> </div><div> 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</div>
<div>The unit aims to introduce 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.</div><div> </div><div>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.</div>
<ul> <li><span style="font-size:12px">Describe fundamental concepts in machine learning (including supervised and unsupervised learning, classification, regression and clustering), and essential elements for building a machine learning system.</span></li> <li><span style="font-size:12px">Explain key supervised learning techniques, compare their differences (including limitations and advantages, quality and trade-offs, commercial/industrial concerns). Apply the knowledge to select suitable techniques for a particular application.</span></li> <li><span style="font-size:12px">Explain key clustering algorithms and their applications.</span></li> <li><span style="font-size:12px">Describe fundamental concepts in model evaluation and selection, explain the training, validation and testing processes, hyperparameter selection approaches. Apply the knowledge to use data, design machine learning experiments, and make observations from results.</span></li> <li><span style="font-size:12px">Design and implement machine learning solutions to real-world problems, evaluate the solution, analyse results and implication.</span></li> <li><span style="font-size:12px">Recognise and describe issues in machine learning.</span></li></ul>
Analytical skills
Project management
Problem solving
Written communication
<ul> <li> Machine Learning Basics</li> <li> k-Nearest Neighbours</li> <li> Machine Learning Experiments</li> <li> Machine Learning Models</li> <li> Loss Functions</li> <li> Training and Optimisation</li> <li> Artificial Neural Networks</li> <li> Support Vector Machines</li> <li> Clustering Analysis</li> <li> Deep Learning Models</li></ul>
<div><p style="margin-bottom:11px"><span style="font-size:11pt"><span style="line-height:107%"><span style="font-family:Calibri,sans-serif">Weekly lectures with structured input and exploratory activities. These will be organised as a blend of brief presentations, tutorial question and practice activities, discussions of materials and tasks that are available online, and question-answer sessions. </span></span></span></p><p style="margin-bottom:11px"> </p><p style="margin-bottom:11px"><span style="font-size:11pt"><span style="line-height:107%"><span style="font-family:Calibri,sans-serif">Bi-weekly laboratories will be drop-in help desks where GTAs provide support for problems provided in lab scripts. These will also be used as surgeries to provide feedback on assessments and as an opportunity to ask questions about the set tasks, and learning materials. Lab scripts contain assessments on mathematical programming for supporting basic machine learning model implementation, also design, implementation and analysis of machine learning techniques for real-world applications.</span></span></span></p></div>
1
Written exam
70%
8
Practical skills assessment
30%
<p><span style="font-size:11.0pt"><span style="font-family:"Microsoft Sans Serif",sans-serif"><span style="color:black"><span style="layout-grid-mode:line">Cohort-level feedback after marking and individual feedback provided by GTA upon request.</span></span></span></span></p>
COMP11120
Mathematical Techniques for Computer Science
Pre-Requisite
Compulsory
COMP13212
Data Science
Pre-Requisite
Compulsory
COMP24011
Introduction to AI
Co-Requisite
Compulsory
MATH11121
Mathematical Foundations & Analysis
Pre-Requisite
Compulsory
Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.COMP11120, COMP13212, MATH11121, COMP24011 are pre/Co req of COMP24112<div>Pre-requisites</div><div> </div><div>To enrol students are required to have taken COMP11120 and COMP13212. Or, if you are on a Computer Science and Maths programme you must have taken MATH11121. COMP24011 is a co-requisite.</div>
Y
<div> 1. Pattern Recognition and Machine Learning. Bishop, Christopher M. ISBN: 978-0387-31073-2. PUblished by springer. 2006. Core material.</div><div> </div><div> 2. Introduction to machine learning. Alpaydin, E. ISBN: 978-0-262-02818-9. Published by The MIT Press. 2014.</div><div> </div><div> 3. Machine learning: A probabilistic perspective. Murphy, Kevin P. ISBN: 978-0-262-01802-9. Published by The MIT Press. 2012.</div><div> </div><div> <a href="http://studentnet.cs.manchester.ac.uk/syllabus/index.php?code=COMP24112&year=2020">COMP24112 reading lists</a> can be found on the Department of Computer Science internal pages</div>
Assessment written exam
2
Lectures
22
Practical classes & workshops
12
0
64