CS342-15 Machine Learning

Academic year
21/22
Department
Computer Science
Level
Undergraduate Level 3
Module leader
Victor Sanchez
Credit value
15
Module duration
10 weeks
Assessment
Multiple
Study location
University of Warwick main campus, Coventry
Introductory description

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

Module aims

It will cover some of the main models and algorithms for regression, classification, clustering and probabilistic classification. Topics such as linear and logistic regression, regularisation, probabilistic (Bayesian) inference, SVMs and neural networks, clustering and dimensionality reduction. The module will use primarily 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

Learning outcomes

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

Indicative reading list
Subject specific skills

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

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 9 sessions of 1 hour (6%)
Private study 111 hours (74%)
Total 150 hours
Private study description

Background reading on wireless networks.
Reading of supplemental 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 D2
Weighting Study time
Individual practical assignment 40%

Individual practical assignment. This assignment is worth more than 3 CATS and is not, therefore, eligible for self-certification.

On-campus Examination 60%

CS342 Exam

~Platforms - AEP


  • Answerbook Pink (12 page)
  • Students may use a calculator
Assessment group R1
Weighting Study time
In-person Examination - Resit 100%

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 CS136 and CS137 or be able to show that they have studied equivalent relevant content.

Courses

This module is Optional for:

  • UCSA-G4G1 Undergraduate Discrete Mathematics
    • Year 3 of G4G1 Discrete Mathematics
    • Year 3 of G4G1 Discrete Mathematics
  • Year 3 of UCSA-G4G3 Undergraduate Discrete Mathematics
  • Year 4 of UCSA-G4G2 Undergraduate Discrete Mathematics with Intercalated Year
  • USTA-G1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)
    • Year 3 of G1G3 Mathematics and Statistics (BSc MMathStat)
    • Year 4 of G1G3 Mathematics and Statistics (BSc MMathStat)
  • USTA-G1G4 Undergraduate Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)
    • Year 4 of G1G4 Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)
    • Year 5 of G1G4 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)
  • UCSA-G500 Undergraduate Computer Science
    • Year 3 of G500 Computer Science
    • Year 3 of G500 Computer Science
  • UCSA-G502 Undergraduate Computer Science (with Intercalated Year)
    • Year 4 of G502 Computer Science with Intercalated Year
    • Year 4 of G502 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 G503 Computer Science MEng
  • 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)
  • USTA-G302 Undergraduate Data Science
    • Year 3 of G302 Data Science
    • Year 3 of G302 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:

  • USTA-GG14 Undergraduate Mathematics and Statistics (BSc)
    • Year 3 of GG14 Mathematics and Statistics
    • Year 3 of GG14 Mathematics and Statistics
  • Year 4 of USTA-GG17 Undergraduate Mathematics and Statistics (with Intercalated Year)