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Throughout the 2021-22 academic year, we will be prioritising face to face teaching as part of a blended learning approach that builds on the lessons learned over the course of the Coronavirus pandemic. Teaching will vary between online and on-campus delivery through the year, and you should read guidance from the academic department for details of how this will work for a particular module. You can find out more about the University’s overall response to Coronavirus at: https://warwick.ac.uk/coronavirus.

CS3D2-30 Machine Learning and Data Mining (DA)

Department
Computer Science
Level
Undergraduate Level 3
Module leader
Adam Chester
Credit value
30
Module duration
6 weeks
Assessment
100% coursework
Study location
University of Warwick main campus, Coventry
Introductory description

This module will introduce students to the different Machine Learning tools available and how to apply them effectively. They will also learn how to interact with sources of data, such as those on the Internet, and produce full solutions for simple applications using Machine Learning.

Module aims

This module aims to provide an in-depth introduction to the foundations and techniques underlying Machine learning and Data Science. It covers a variety of learning algorithms and approaches for evaluating their performance. Students will also be taught how to apply some of the main methods and standards for data scraping and data sharing.

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.

This module will cover:

  • Linear Regression, Logistic Regression, and Probabilistic Inference
  • Artificial Neural networks and Deep Learning
  • Clustering Algorithms, Dimensionality Reduction, and Multi-dimensional Scaling
  • Data pre-processing and basic data transformation
Learning outcomes

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

  • Understand and apply basic concepts of Linear regression and Gaussian Density.
  • Understand and apply Bayesian Learning.
  • Understand and apply Markov Chain Monte Carlo Sampling.
  • Understand and apply selected topics on Artificial Neural Networks and Deep Learning.
  • Understand and apply selected topics in advanced Machine Learning algorithms.
  • Demonstrate practical skills in knowledge representation and data capture and analysis.
Indicative reading list

Tan, et al., "Introduction to Data Mining (2/e)", Pearson (2019)
Rogers et al., "A First Course in Machine Learning (2/e)", Chapman and Hall/CRC (2017)

Subject specific skills
  • Identify organisational information requirements and model data solutions using conceptual data modelling techniques
  • Able to manage data effectively and undertake data analysis
  • Import, cleanse, transform, and validate data with the purpose of understanding or making conclusions from the data for business decision making purposes
  • Present data visualisation using charts, graphs, tables, and more sophisticated visualisation tools
  • Perform routine statistical analyses and ad-hoc queries
  • Use a range of analytical techniques such as data mining, time series forecasting and modelling techniques to identify and predict trends and patterns in data
  • Report on conclusions gained from analysing data using a range of statistical software tools
  • Summarise and present results to a range of stakeholders making recommendations
  • The quality issues that can arise with data and how to avoid and/or resolve these
  • How to use and apply industry standard tools and methods for data analysis
Transferable skills
  • Applies analytical and critical thinking skills to Technology Solutions development and to systematically analyse and apply structured problem solving techniques to complex systems and situations
  • Have demonstrated that they have mastered basic business disciplines, ethics and courtesies, demonstrating timeliness and focus when faced with distractions and the ability to complete tasks to a deadline with high quality.
  • Flexible attitude
  • Ability to perform under pressure
  • A thorough approach to work

Study time

Type Required
Lectures 30 sessions of 1 hour (10%)
Seminars 44 sessions of 1 hour (15%)
Tutorials 14 sessions of 1 hour (5%)
Work-based learning 172 sessions of 1 hour (57%)
Other activity 40 hours (13%)
Total 300 hours
Private study description

No private study requirements defined for this module.

Other activity description

Self study

Costs

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Assessment group C
Weighting Study time
Online tests based on work-based learning 50%
Machine Learning and Data Mining 50%
Feedback on assessment

Online testing provides instant written feedback, written and oral otherwise

There is currently no information about the courses for which this module is core or optional.