Artificial Intelligence and Machine Learning: Theory and Practice
Lady Margaret Hall, University of Oxford
Key Information
Campus location
Oxford, United Kingdom
Languages
English
Study format
Distance Learning, On-Campus
Duration
3 weeks
Pace
Full time
Tuition fees
GBP 3,980 / per course
Application deadline
10 May 2024
Earliest start date
24 Jun 2024
Introduction
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.
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
Gallery
Ideal Students
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 in coding is recommended. Prior experience in artificial intelligence, machine learning, or the Python programming language is not required.
Admissions
Scholarships and Funding
Lady Margaret Hall does not offer scholarships or grants for participation in the LMH Summer Programmes, but many students find they are able to seek financial assistance from their home university or academic department. The best first point of contact is likely the Study Abroad / International Education Office at your university.
Program Outcome
By the end of this course, you will:
- Understand theoretical concepts of artificial intelligence and machine learning.
- Know how basic artificial intelligence and machine learning tools are used in practice.
- Know how to implement basic algorithms and train small networks for practical problems.
- Be able to identify and use relevant artificial intelligence and machine learning tools in research.
- Know how to implement and deploy artificial intelligence and machine learning algorithms on Google Cloud.