Module code: MTH786U
Credits: 15.0
Semester: SEM1
Contact: Dr Nicola Perra
This course aims at providing students with Machine Learning skills based on the Python programming language as it is currently used in industry. Some of the presented methods are regression and classification techniques (linear and logistic regression, least-square); clustering; dimensionality reduction techniques such as PCA, SVD and matrix factorization. More advanced methods such as generalized linear models, neural networks and Bayesian inference using graphical models are also introduced. The course is self-contained in terms of the necessary mathematical tools (mostly probability) and coding techniques. At the end of the course, students will be able to formalize a ML task, choose the appropriate method in order to tackle it while being able to assess its performance, and to implement these algorithms in Python.
Connected course(s): UDF DATA
Assessment: 60.0% Coursework, 40.0% Examination
Level: 7
