Artificial Intelligence and Machine Learning: Theory and Practice

This course is designed to introduce students to the basic concepts of machine learning (ML) and artificial intelligence (AI) in a hands-on manner. The course functions in a self-contained manner with only basic knowledge of calculus and linear algebra required. Prior knowledge of machine learning and artificial intelligence is not essential.

The course will begin with a quick introduction to Python and the theoretical foundations of basic concepts in machine learning and artificial intelligence. Students will start with a simple linear regression example where they will derive and implement the gradient descent for a curve fitting problem and try to understand the concepts of loss function, regularization techniques, and bias-variance trade-off. Students will then be introduced to stochastic gradients descent and will implement stochastic gradient descent for regression using TensorFlow and PyTorch.

Students will design simple neural networks for MNIST classification and implement the full forward and backward pass for the training of the neural network. Following which students will be introduced to Convolutional Neural Networks and will implement MNIST classification with CNNs. Students will understand how PyTorch and TensorFlow handles the forward and backward pass during training. In the final part of the course, large scale problems of semantic segmentation, edge detection and metric learning will be implemented on AWS/ Google cloud.

As exercises for the course, the students will try to solve small scale practical problems of machine learning and artificial intelligence from diverse domains.

Click here for the Course Overview.

By the end of the course, the students will:

• Understand the theory of machine learning and artificial intelligence
• Know about ML and AI tools used in practice
• Know how to implement basic algorithms of AI and ML and train small networks for practical problems
• Be able to identify and use relevant AI and ML tools in their research
• Know how to implement and deploy ML and AI algorithms on AWS/Google cloud.

This course would suit STEM students in undergraduate or entry-level postgraduate study. Basic knowledge of calculus and linear algebra is required, and some experience of coding is recommended. Prior knowledge of Artificial Intelligence, Machine Learning, or the Python programming language is not required.

Available as a Residential or Online course on the following dates:

26th June 2023 to 15th July 2023

7th August 2023 to 25th August 2023

Find out more about the admissions criteria, programme fees, and how to apply.

Apply for a residential programme.

Apply for an online programme.

If you have any questions, or would like to know more, please do get in touch here.