To equip students with knowledge of the basic principles, techniques, algorithms, implementation and applications of Machine Learning.
A. Demonstrate a comprehensive understanding of the theoretical aspects of the learning problems that ML algorithms try to solve. B. Critically reflect on the properties of ML algorithms. C. Apply ML algorithms to practical learning problems. D. Identifying and customizing the features and the hyperparameters of ML algorithms to solve practical learning problems.
Students will be expected to attend two hours of a formal lecture and two hours of a lab section in a typical week. Lectures will introduce students to the academic content. Labs will be used to apply the lecture materials in Python programming. In addition, students will be expected to devote unsupervised time to private study. Private study will provide time for reflection and consideration of lecture material and background reading. The first assessment (CW1) will assess how well students keep up with the material presented in the lectures. The second assessment (CW2) will assess how well students keep up with the material presented in the labs. A final project involving both theoretical and practical materials (CW3) at the end of the module will assess the academic achievement of students.