CS909-15 Data Mining

Academic year
24/25
Department
Computer Science
Level
Taught Postgraduate Level
Module leader
Fayyaz ul Amir Afsar Minhas
Credit value
15
Module duration
10 weeks
Assessment
Multiple
Study location
University of Warwick main campus, Coventry
Introductory description

Data Mining.

Module aims

Understanding of the value of data mining in solving real-world problems;
Understanding of foundational concepts underlying data mining;
Understanding of algorithms commonly used in data mining tools;
Ability to apply data mining tools to real-world problems.

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.

Introduction to machine learning, basic concepts and motivation;
Data pre-processing and basic data transformations;
Regression models (linear regression, logistical regression);
Classification: decision trees, probabilistic generative models;
Model evaluation, bias-variance trade-off;
Ensemble methods: boosting, bagging & random forests;
Dimensionality reduction: Principal Component Analysis (PCA), T-distributed Stochastic Neighbour Embedding (t-SNE);
Introduction to deep learning, backpropagation, gradient descent;
Convolutional neural networks;
Word embeddings;
Sequence-to-sequence models;
Attention mechanisms and memory networks;
Unsupervised deep learning and generative models;
Transfer learning.

Learning outcomes

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

Indicative reading list

Please see Talis Aspire link for most up to date list.

View reading list on Talis Aspire

Research element

The students shall be required to explore the literature about latest methods related to classification and deep learning

Interdisciplinary

Data mining lies at the intersection of statistics, computer science and mathematics.

Subject specific skills

Design of data mining solutions
Learning to develop novel algorithms related to machine learning
Conducting proper experiment design in machine learning

Transferable skills

Experiment design
Critical Thinking
How to conduct literature reviews

Study time

Type Required
Lectures 30 sessions of 1 hour (20%)
Practical classes 10 sessions of 1 hour (7%)
Private study 110 hours (73%)
Total 150 hours
Private study description

Private study should focus on the following components:

a. Assigned reading
b. Coding exercises
c. Assignment solution
d. Review of the lab component
e. Revision of the lecture slides

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
Assignment 2 35%

Assignment 2. This assignment is worth more than 3 CATS and is not, therefore, eligible for self-certification.

Assignment 1 25%

Assignment 1. This assignment is worth more than 3 CATS and is not, therefore, eligible for self-certification.

In-person Examination 40%

CS909 Examination


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

CS909 MSc resit examination


  • Answerbook Pink (12 page)
  • Students may use a calculator
Feedback on assessment

Formative feedback will be provided in lab sessions and also during lectures where answers are given in class to short exercises.

Summative feedback:

Past exam papers for CS909

Pre-requisites

No Warwick module is required as pre-requisite. However familiarity with basic probability and statistics (for example: discrete and continuous random variables, densities and distributions, common distributions including Bernoulli, binomial, uniform and normal distribution, expectations) will be needed.

Courses

This module is Core for:

  • Year 1 of TPSS-C803 Postgraduate Taught Behavioural and Data Science
  • Year 1 of TCSA-G5PA Postgraduate Taught Data Analytics
  • Year 1 of TCSA-G5PB Postgraduate Taught Data Analytics (CUSP)

This module is Optional for:

  • Year 2 of TIMS-L990 Postgraduate Big Data and Digital Futures
  • Year 1 of TESA-H641 Postgraduate Taught Communications and Information Engineering
  • TCSA-G5PD Postgraduate Taught Computer Science
    • Year 1 of G5PD Computer Science
    • Year 1 of G5PD Computer Science
  • TIMA-L995 Postgraduate Taught Data Visualisation
    • Year 1 of L995 Data Visualisation
    • Year 2 of L995 Data Visualisation
  • Year 1 of TMAA-G1PF Postgraduate Taught Mathematics of Systems
  • TESA-H1B1 Postgraduate Taught Predictive Modelling and Scientific Computing
    • Year 1 of H1B1 Predictive Modelling and Scientific Computing
    • Year 2 of H1B1 Predictive Modelling and Scientific Computing
  • Year 1 of TSTA-G4P1 Postgraduate Taught Statistics
  • Year 1 of TIMA-L99D Postgraduate Taught Urban Analytics and Visualisation

This module is Option list A for:

  • Year 5 of UCSA-G504 MEng Computer Science (with intercalated year)
  • Year 1 of TIMS-L990 Postgraduate Big Data and Digital Futures
  • Year 1 of TMAA-G1PF Postgraduate Taught Mathematics of Systems
  • UCSA-G503 Undergraduate Computer Science MEng
    • Year 4 of G503 Computer Science MEng
    • Year 4 of G503 Computer Science MEng