Executive Certificate of Machine Learning

Aims of the module:

The module covers computational algorithms for learning from data, data-driven decision making and complex problem solving. This module will look at the underlying principles of machine learning, the advantages and limitations of the various approaches and effective ways of applying them.

Content of the module:

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. In this module, the students will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work. More importantly, they will learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

Theoretical concepts will be addressed during the weekly taught lectures. Al lecture slides will be provided on the Blackboard prior to each session. Examples will be presented in class that provide an opportunity for the students to gauge their understanding and to request clarification from the lecturer.

There are seminar sessions scheduled in which the student will start working with data and algorithms. Relevant programming languages (e.g. Python) will be used for model developments.

At the end of this module, you will be able to:

1. Demonstrate a comprehensive understanding of the principles and practices of machine learning.

2. Demonstrate an advanced level of knowledge of machine learning methods and of the processes involved in their deployment.

3. Apply deep understanding of machine learning paradigms to solve data modelling, classification and decision-making problems.

4. Make a critical analysis of problem requirements relating to the application of machine learning

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