28 August 2020
Artificial Intelligence and Machine Learning: Theory and Practiceonline course
Lady Margaret Hall, a College of the University of Oxford is excited to offer a new topical online course with a practical hands-on programme for students interested in artificial intelligence and machine learning.
The course will begin with a quick introduction of 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. The students will then be introduced to stochastic gradients descent and will implement stochastic gradient descent for regression using TensorFlow and Pytorch.
The 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 the students will be introduced to Convolutional Neural Networks and will implement MNIST classification with CNNs. The student will understand how Pytorch and TensorFlow handles the forward and backward pass during training.
As exercises for the course, the students will try to solve small scale practical problems of machine learning and artificial intelligence from diverse domains.
To benefit from the course students need a basic knowledge of calculus and linear algebra. No prior knowledge of machine learning and artificial intelligence is essential.
Dr Naeemullah Khan, Postdoctoral Research Scientist, Department of Engineering, University of Oxford
The course is open to undergraduate and post-graduate students who want to get an introduction to Artifical Intelligence and Machine Learning.
We will consider applicant’s academic ability and expect them to have as a minimum GPA 3.2 out of the 4.0 grading scale, or 80 out of the 100 grading scale.
In order to benefit from the course, knowledge of basic calculus and linear algebra is required.
To fully participate in the course all students should have sufficient proficiency in English.
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
By the end of the course, the students will:
o Understand the theory of machine Learning and artificial Intelligence
o Know about ML and AI tools used in practice
o Implement basic algorithms of AI and ML and train small networks for practical problems
o Be able to identify and use relevant tools of AI and ML in their research
GBP 700: Course fee