About this course
From self-driving cars and augmented reality to intelligent medical imaging helping doctors identify diseases more quickly, computer vision is a rapidly-growing field within artificial intelligence and machine learning. In this course, students who are already familiar with the key theoretical foundations of artificial intelligence and machine learning will dive deeper into the exciting capabilities of this area of research and its applications.
You will begin with computer vision algorithms for classification, recognition, detection, and their implementation in deep learning libraries, before exploring autoencoders and variational autoencoders, and gaining insights into the training and application of generative adversarial networks. You will proceed to an in-depth examination of diffusion models, including score-based diffusion models, latent diffusion models, and Stable Diffusion. The final part of the course explores even more advanced topics, including the representation of 3D objects, vision transformers, video classification, and text to image generation.
This intensive course offers students theoretical understanding and practical experience in a range of advanced computer vision concepts and techniques, offering career skills as well as excellent foundations for future research.
By the end of this course, you will:
- Understand computer vision algorithms for classification, recognition, and detection, and their implementation in deep learning libraries.
- Know the different types of generative adversarial network and their distinct contributions to controlled data synthesis and image generation.
- Be able to identify different diffusion models and assess their advantages in generative modeling.
- Be able to demonstrate awareness and understanding of the latest key research areas in computer vision.
Who is this course suitable for?
This course would suit STEM students with intermediate level experience in artificial intelligence, machine learning, and computer vision concepts and techniques, including those undertaking, or looking ahead to, graduate level study or research.
Specifically, students on this course must have experience of the following topics:
- Knowledge of the deep learning libraries.
- Understanding of deep learning and convolutional neural networks.
- Strong background in optimization and probability.
- Familiarity with the Python programming language.
Dates and availability
Available as a Residential or Online course on the following date:
Session 2: 15th July to 2nd August 2024
Click below to find out how to apply.
Get in touch
If you have any questions, or would like to know more, please get in touch via the link below.