Lady Margaret Hall, University of Oxford
Artificial Intelligence and Machine Learning: Theory and Practice

Oxford, United Kingdom

DEGREE TYPE

Summer Course

DURATION

3 weeks

LANGUAGES

English

PACE

Full time

APPLICATION DEADLINE

EARLIEST START DATE

STUDY FORMAT

Distance Learning, On-Campus

* Applications are processed and offers are made on a rolling basis, and many courses fill up far in advance of the deadline so we recommend applying as early as possible.

Key Summary

About : The Artificial Intelligence and Machine Learning: Theory and Practice program offers an in-depth exploration of AI and machine learning concepts, methodologies, and applications. Students will engage with both theoretical foundations and practical implementations, equipping them with the skills needed in a rapidly evolving field. The curriculum is designed for those looking to deepen their understanding and expertise in AI technologies and data science.

Career Outcomes : Graduates can pursue various roles including data scientists, machine learning engineers, or AI researchers. Opportunities exist in sectors like technology, finance, healthcare, and academia, addressing the growing demand for skilled professionals in the field of AI and machine learning.

In our age of burgeoning smart technology and automation, we are already seeing the transformative potential of Artificial Intelligence and Machine Learning in fields as diverse as finance, medicine, and manufacturing. This course offers a hands-on introduction to this future-focused area of research.

You will begin with an introduction to the basics of programming in Python, in particular understanding object-oriented programming and its importance to deep learning. You will quickly proceed to an introduction to artificial intelligence, examining the fundamentals of supervised machine learning, including linear regression, logistic regression, neural networks, and gradient descent. In the second week of the course, you will explore image processing, investigating transformations, convolutional filters, and edge detection, before an introduction to convolutional neural networks and some prominent CNN architectures such as VGG and ResNet. In the final part of the course, you will look at the core concepts of natural language processing, including sequence modeling, autoregressive models, and recurrent neural networks.

This intensive course offers both a theoretical introduction to artificial intelligence and machine learning concepts and an opportunity to put this knowledge into action in solving small-scale practical problems from diverse domains.