CS909-15 Data Mining
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:
- 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.
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 | Eligible for self-certification | |
|---|---|---|---|
| Assignment 2 | 35% | No | |
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Assignment 2. This assignment is worth more than 3 CATS and is not, therefore, eligible for self-certification. |
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| Assignment 1 | 25% | No | |
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Assignment 1. This assignment is worth more than 3 CATS and is not, therefore, eligible for self-certification. |
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| In-person Examination | 40% | No | |
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CS909 Examination
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Assessment group R1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| In-person Examination - Resit | 100% | No | |
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CS909 MSc resit examination
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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.
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
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TCSA-G5PD Postgraduate Taught Computer Science
- Year 1 of G5PD Computer Science
- Year 1 of G5PD Computer Science
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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
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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
- Year 4 of UCSA-G503 Undergraduate Computer Science MEng