Oxford, United Kingdom

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.

Target group

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.

Course aim

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.

Credits info

7.5 EC
LMH Summer Programmes are designed to be eligible for credit, and we recommend the award of 7.5 ECTS / 4 US / 15 CATS for this course.

Fee info

GBP 1300: This includes:
- All tuition, including lectures, seminars, and tutorials.
- Assessment, transcript of academic performance, and certificate.
- Access to the LMH Summer Programmes remote learning platform.
- Support of the dedicated Remote Learning Coordinator.