Advanced Artificial Intelligence and Machine Learning: Deep Unsupervised Learning
Lady Margaret Hall, University of Oxford
Oxford, United Kingdom
Distance Learning, On-Campus
GBP 3,980 / per course **
10 May 2024*
Earliest start date
05 Aug 2024
* 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.
** For a 3-week residential programme the fee is £3,980, including accommodation and meals. For a 3-week online programme the fee is £1,360.
Deep Unsupervised Learning is an exciting emerging area of research in the field of artificial intelligence and machine learning, in which the goal is to develop systems that can learn from unlabelled data. Such systems closely mimic natural human intelligence by finding patterns in data without instructions on what to look for.
The course will begin with an introduction to unsupervised learning and clustering algorithms, before exploring generative adversarial networks and deep generative models. You will examine self-supervised learning, anomaly detection, flow-based models, and unsupervised representation learning. The final part of the course focuses on clustering in high-dimensional spaces, semi-supervised learning, energy-based models, and unsupervised learning for reinforcement.
This intensive course offers theoretical understanding and practical experience with a focus on real-world applications of deep unsupervised learning across various domains, offering career skills as well as excellent foundations for future research.
Dates and Availability
Available as a Residential or Online course on the following dates:
Session 3: 5th August to 23rd August 2024
This course would suit STEM students with intermediate-level experience in artificial intelligence and machine learning concepts and techniques, including those undertaking, or looking ahead to, graduate-level study or research.
Specifically, students in this course must have experience with the following topics:
- Knowledge of the deep learning libraries.
- Understanding of deep learning, recurrent neural networks, and convolutional neural networks.
- Strong background in optimization and probability.
- Familiarity with the Python programming language.
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.
By the end of this course, you will:
- Understand the differences between supervised and unsupervised learning and the fundamentals of clustering.
- Be able to utilize a range of algorithms and techniques for unsupervised, self-supervised, and semi-supervised learning.
- Be able to evaluate the efficacy of real-world applications of deep unsupervised learning across various domains.
- Be able to demonstrate familiarity with the current state of research into deep unsupervised learning.