Programme dates: Monday 5 July – Friday 16 July 2021
Course format: Online course
Teaching language: English (please see our language requirements below))
Study / time commitment: 10 days, Monday – Friday, live 9:00 – 13:00 UK time
Costs: Early bird fee by 4 June 2021 - £ 750 / after 4 June - £ 900
Application deadline: 30 June 2021
COURSE IS FULL
To be included on a waiting list, please email email@example.com
About the course
This course is designed to introduce students to the basic concepts of machine learning (ML) and artificial intelligence (AI) in a hands-on manner. We have developed the course 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.
Benefits / Outcomes
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.
“Thank you very much for the course. It was extremely interesting and fun!”
“Thank you so much for organising and teaching this course. I believe I've learnt more useful and interesting things in these 10 days than I usually do in a much longer period of time.”
“I really enjoyed the course and found it so interesting. Also thank you so much for answering all of my questions, I really appreciate it.”
Programme structure / Teaching methods
This is an intensive hands-on course consisting of 10 full days of teaching and practical sessions. Every day will start with a 4-hour teaching session, from 9:00 - 13:00 UK time, where the instructor will explain theory and go over the exercises for each day. Following the instruction the students will be expected to work independently on their daily exercises for approximately 2 hours each day. The instructor will be available online during this time to support students in their independent work and to provide advice where needed.
Dr Naeemullah Khan, Research Fellow, Lady Margaret Hall, Postdoctoral Research Scientist, Department of Engineering, University of Oxford.
Course requirements / Is the course right for you?
The course is open to undergraduate and post-graduate students who want to get an introduction to Artificial Intelligence and Machine Learning.
In order to benefit from the course, knowledge of basic calculus and linear algebra is required.
To fully participate in the course all students will need to have proficiency in English to the following standards:
English language requirements for non-native English speaking students: Overall TOEFL score of 85; or IELTS score of 6.5 (no less than 6.0 in each component); or CET-4 at 550 or CET-6 at 520.
There are no other formal academic entry criteria but we expect students to have a high level of commitment to their study.