Introductory description

This module aims to provide students with an in-depth introduction to two main- areas of Machine Learning: supervised and unsupervised learning.

Module aims

The module covers the main models and algorithms for regression, classification, clustering, and probabilistic classification. Topics such as linear and logistic regression, regularisation, probabilistic (Bayesian) inference, SVMs, neural networks, clustering, and dimensionality reduction are covered. The module primarily uses the Python programming language and assumes familiarity with linear algebra, probability theory, and programming in Python.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Intro to Supervised/Unsupervised Learning

Decision Trees
Linear regression: regularization, linear classifiers
Logistic Regression, multi-class logistic regression, Support Vector Machines
Feature selection latent factor models (PCA)
Clustering (k-means, soft k-means)
Ensemble methods such as Random Forest, AdaBoost, and XGBoost
Probabilistic methods (Bayesian view)
Model evaluation and model selection
Introduction to neural networks
Autoencoders

Learning outcomes

By the end of the module, students should be able to:

Develop an appreciation for what is involved in models that are optimised based on data
Understand a wide variety of learning algorithms
Understand how to evaluate models optimised on data
Understand applications of models to real data and problems.

Indicative reading list

Reading lists can be found in Talis

Subject specific skills

Understand the concept of learning in computer science.
Understand the difference between supervised and unsupervised learning.
Understand the difference between machine learning and deep learning.
Design and evaluate machine and deep learning models.

Transferable skills

Mathematical analysis of learning methods.
Evaluation of algorithms.
Programming skills in python.

Study time

Type	Required
Lectures	30 sessions of 1 hour (20%)
Practical classes	8 sessions of 1 hour (5%)
Private study	112 hours (75%)
Total	150 hours

Private study description

Background reading on statistics and probability.
Reading supplementary material to reinforce the concepts covered in class.
Revision of concepts covered in class.

Costs

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Students can register for this module without taking any assessment.

Assessment group D4

	Weighting	Eligible for self-certification
Individual practical assignment	40%	No
Individual practical assignment. This assignment is worth more than 3 CATS and is not, therefore, eligible for self-certification.
Centrally-timetabled examination (On-campus)	60%	No
CS342 Exam Answerbook Pink (12 page) Students may use a calculator

Assessment group R3

	Weighting	Study time	Eligible for self-certification
In-person Examination - Resit	100%		No
CS342 resit examination Answerbook Pink (12 page) Students may use a calculator

Feedback on assessment

Feedback via Tabula for coursework

Past exam papers for CS342

Pre-requisites

Students must have studied CS130 and CS131 OR CS146 and CS147 or be able to show that they have studied equivalent relevant content.

Courses

This module is Optional for:

Year 3 of UCSA-G4G1 Undergraduate Discrete Mathematics
UCSA-G4G3 Undergraduate Discrete Mathematics
- Year 3 of G4G1 Discrete Mathematics
- Year 3 of G4G3 Discrete Mathematics
Year 4 of UCSA-G4G4 Undergraduate Discrete Mathematics (with Intercalated Year)
Year 4 of UCSA-G4G2 Undergraduate Discrete Mathematics with Intercalated Year
Year 5 of USTA-G1G4 Undergraduate Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)

This module is Option list A for:

Year 4 of UCSA-G504 MEng Computer Science (with intercalated year)
Year 3 of UCSA-G500 Undergraduate Computer Science
Year 4 of UCSA-G502 Undergraduate Computer Science (with Intercalated Year)
UCSA-G503 Undergraduate Computer Science MEng
- Year 3 of G500 Computer Science
- Year 3 of G503 Computer Science MEng
Year 3 of USTA-G302 Undergraduate Data Science
Year 3 of USTA-G304 Undergraduate Data Science (MSci)
Year 4 of USTA-G303 Undergraduate Data Science (with Intercalated Year)

This module is Option list B for:

Year 3 of UCSA-G406 Undergraduate Computer Systems Engineering
Year 3 of UCSA-G408 Undergraduate Computer Systems Engineering
Year 4 of UCSA-G407 Undergraduate Computer Systems Engineering (with Intercalated Year)
Year 4 of UCSA-G409 Undergraduate Computer Systems Engineering (with Intercalated Year)
Year 4 of USTA-G1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)
Year 3 of USTA-GG14 Undergraduate Mathematics and Statistics (BSc)
Year 4 of USTA-GG17 Undergraduate Mathematics and Statistics (with Intercalated Year)

This module is Option list D for:

Year 3 of USTA-G1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)
Year 4 of USTA-G1G4 Undergraduate Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)

CS342-15 Machine Learning