CS429-15 Data Mining
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.
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;
Attention mechanisms and memory networks;
Unsupervised deep learning and generative models;
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 self-directed 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.
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.
Subject specific skills
Design of data mining solutions
Learning to develop novel algorithms related to machine learning
Conducting proper experiment design in machine learning
How to conduct literature reviews
|Lectures||30 sessions of 1 hour (20%)|
|Practical classes||10 sessions of 1 hour (7%)|
|Private study||110 hours (73%)|
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
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
Assignment 2. This assignment is worth more than 3 CATS and is not, therefore, eligible for self-certification.
Assignment 1. This assignment is worth more than 3 CATS and is not, therefore, eligible for self-certification.
CS429 MEng Examination.
~Platforms - AEP
Assessment group R1
CS429 MEng resit examination
~Platforms - AEP
Feedback on assessment
Formative feedback will be provided in lab sessions and also during lectures where answers are given in class to short exercises.
- Written feedback will be provided on the practical assignment and will be given electronically with explanation on the mark given.
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.
This module is Optional for:
- Year 5 of UCSA-G504 MEng Computer Science (with intercalated year)
- Year 1 of TESA-H641 Postgraduate Taught Communications and Information Engineering
- Year 4 of UCSA-G503 Undergraduate Computer Science MEng
This module is Option list B for:
- Year 4 of UCSA-G408 Undergraduate Computer Systems Engineering
- Year 4 of UCSA-G4G3 Undergraduate Discrete Mathematics