Course Credit:
3
Introduction
The course is typically designed for students who have a basic understanding of mathematics,
programming, and machine learning concepts. The course provides a broad but thorough
introduction to the methods and practice of advanced statistical machine learning and its
core methods, models, and algorithms.
24
Objectives
The aim of the course is to provide students of Applied Statistics and Data Science with de-
tailed knowledge of how advanced Machine Learning methods work and how statistical mod-
els can be brought to bear in computer systems not only to analyze large, high-dimensional,
unstructured, and big data sets but also to let computers perform tasks e ciently, that tra-
ditional methods of statistical and computer science are unable to address. Students will
understand the underlying theory and perform assignments that involve a variety of real-
world datasets from a variety of domains. They will learn recent statistical techniques based
on a synthesis of resampling techniques, and neural networks that have achieved remarkable
progress and led to a great deal of commercial and academic interest.
Learning Outcomes
After successful completion of the course, students are expected to (i) Describe a number
of advanced machine learning techniques including deep learning, neural network and rein-
forcement machine learning, (ii) Assess the strength and weaknesses of each of these models,
(iii) Know the underlying mathematical relationships within and across statistical learning
algorithms, iv) Identify appropriate statistical tools for a data analysis problems in the real
world based on reasoned arguments, v) Develop and implement optimisation algorithms for
advanced models, (vi) Design decision and optimal control problems to improve performance
of statistical learning algorithms, (vii) Design and implement various advanced statistical ma-
chine learning algorithms in real-world applications, (viii) Have an understanding of how to
choose a model to describe a particular type of data, ix) Evaluate the performance of various
advanced statistical machine learning algorithms.