CS42915 Data Mining
Introductory description
Data Mining.
Module aims
Understanding of the value of data mining in solving realworld problems;
Understanding of foundational concepts underlying data mining;
Understanding of algorithms commonly used in data mining tools;
Ability to apply data mining tools to realworld 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 preprocessing and basic data transformations;
Regression models (linear regression, logistical regression);
Classification: decision trees, probabilistic generative models;
Model evaluation, biasvariance tradeoff;
Ensemble methods: boosting, bagging & random forests;
Dimensionality reduction: Principal Component Analysis (PCA), Tdistributed Stochastic Neighbour Embedding (tSNE);
Introduction to deep learning, backpropagation, gradient descent;
Convolutional neural networks;
Word embeddings;
Sequencetosequence 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:
 Display a comprehensive understanding of different data mining tasks and the algorithms most appropriate for addressing them.
 Evaluate models/algorithms with respect to their accuracy.
 Demonstrate capacity to perform a selfdirected piece of practical work that requires the application of data mining techniques.
 Critique the results of a data mining exercise.
 Develop hypotheses based on the analysis of the results obtained and test them.
 Conceptualise a data mining solution to a practical problem.
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 C1
Weighting  Study time  

Assignment 2  25%  
Assignment 2. This assignment is worth more than 3 CATS and is not, therefore, eligible for selfcertification. 

Assignment 1  25%  
Assignment 1. This assignment is worth more than 3 CATS and is not, therefore, eligible for selfcertification. 

Inperson Examination  50%  
CS429 MEng Examination.

Assessment group R1
Weighting  Study time  

Inperson Examination  Resit  100%  
CS429 MEng resit examination

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:
 Written feedback will be provided on the practical assignment and will be given electronically with explanation on the mark given.
Prerequisites
No Warwick module is required as prerequisite. 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 Optional for:
 Year 5 of UCSAG504 MEng Computer Science (with intercalated year)

UCSAG503 Undergraduate Computer Science MEng
 Year 4 of G503 Computer Science MEng
 Year 4 of G503 Computer Science MEng
This module is Option list B for:
 Year 4 of UCSAG408 Undergraduate Computer Systems Engineering
 Year 5 of UCSAG409 Undergraduate Computer Systems Engineering (with Intercalated Year)
 Year 4 of UCSAG4G3 Undergraduate Discrete Mathematics
 Year 5 of UCSAG4G4 Undergraduate Discrete Mathematics (with Intercalated Year)