Data Mining.
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.
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.
By the end of the module, students should be able to:
Please see Talis Aspire link for most up to date list.
View reading list on Talis Aspire
The students shall be required to explore the literature about latest methods related to classification and deep learning
Data mining lies at the intersection of statistics, computer science and mathematics.
Design of data mining solutions
Learning to develop novel algorithms related to machine learning
Conducting proper experiment design in machine learning
Experiment design
Critical Thinking
How to conduct literature reviews
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 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
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.
Weighting  Study time  

Assignment 2  35%  
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  40%  
CS909 Examination

Weighting  Study time  

Inperson Examination  Resit  100%  
CS909 MSc resit examination

Formative feedback will be provided in lab sessions and also during lectures where answers are given in class to short exercises.
Summative feedback:
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.
This module is Core for:
This module is Optional for:
This module is Option list A for: