Machine Learning and Data Science Skills for Data-Driven Decision Making

Overview

Academic Lead: Dr Dimitris Kalogiros

Syllabus: SUM404N Machine Learning and Data Science for Data Driven Decision Making [PDF]

This module's interactive learning sessions allow students to acquire the hands-on and on-screen experience they need in exploring the rapidly evolving landscape of machine learning and data science. Students will work collaboratively to draw conclusions and extract useful information from available datasets while gaining the invaluable skills on how to interpret and report their analysis and results for informed decision making purposes.

This is a practical module that provides an introduction to the concepts of machine learning and application of algorithms to several types of available data samples.  In order to achieve this students will be introduced to the Python programming language and key concepts related to the TensorFlow (TM) programming toolkit from Google.  At the end of the module students will have learned how to train machine learning algorithms and evaluate their performance on research data. Programming skills will be developed during this module in order to explore the potential benefits of deep learning algorithms. 

Course content is subject to change.

Course aims

The module also aims to address the current needs of the prospective students to develop the following most in-demand skills in data science: how to use scientific computing methods to handle, cleanse, transform, and validate data with the purpose of gaining insights from a wide range of datasets; how to present available data using charts, graphs, tables and more sophisticated visualisation tools; how to model data and perform statistical analysis and ad hoc queries; how to report on key findings and useful information extracted from analysed datasets and how to summarise and communicate results to mixed audiences.

Teaching and learning

You will be taught through a combination of lectures, laboratory work, and workshops.

Learning outcomes

You will learn/develop:

• basic commands in Python and learn how to manipulate data using this programming language
• how to use TensorFlow^TM tools to optimise neural networks and convolutional neural networks as examples of machine-learning algorithms
• a comprehension of machine-learning algorithms and their use.

You will develop/be able to:

• understand the principles of optimisation algorithms and the role of activation functions in neural networks
• understand the concept of overtraining of hyperparameters for a machine-learning algorithm, and how that can be spotted using data samples
• understand the concept of the Receiver Operating Characteristic (ROC) curve and how the area under this curve can be used to select models based on the ability to separate signal from background
• demonstrate information expertise through the portfolio of work that you will build during this course, and the application of that portfolio of skills to problem solving
• demonstrate a rounded intellectual development in all aspects of this course, including self-study, directed reading, in-session quizzes to test your incremental assimilation of knowledge and the final critical presentation of what you have learned and achieved during the course
• improve your research capacity via the application of core principles on machine learning to example data sets. This will allow the critical analysis of data in terms of specific problems using modern techniques
• communicate clearly via the oral presentation component, where you will give a five-minute presentation on what you have learned during the course (including the main results you have obtained) and will respond to questions on your presentation.

Fees

Additional costs

All reading material will be provided online, so it is not necessary to purchase any books.

For course and housing fees visit our finance webpage

Entry requirements

We welcome Summer School students from around the world. We accept a range of qualifications

How to apply

Have a question? Get in touch - one of the team will be happy to help!

Applications close 26 May 2025