Summary 
The module is about core technologies underpinning modern
artificial intelligence. The module will introduce
statistical machine learning and probabilistic modelling and
their application to describing realworld phenomena. The
module will give you a grounding in modern state of the
art algorithms that allow modern computer systems to learn
from data. 
Session 
Autumn 2023/24 
Credits 
15 
Assessment 
 Formal examination [LO1, LO2]
 Assignment [LO3, LO4]

Lecturer(s) 
Dr Matthew Ellis & Dr Michael Smith 
Resources 

Aims 
This unit aims to provide a deep understanding of the
fundamental technologies underlying modern artificial
intelligence. In particular, it will provide a foundational
understanding of
 data science,
 machine learning.

Learning Outcomes 
By the end of the unit, a candidate will be able to
demonstrate the ability to
 understand probability theory and how it relates
to uncertainty in modern artificial intelligence,
 understand when and how to implement the appropriate
learning paradigm for a given application,
 implement a range of machine learning algorithms on simple problems,
 deploy appropriate machine learning algorithms to solve realworld problems.

Content 
Typical content will include:
 Introduction to machine learning
 EndtoEnd machine learning
 Linear regression
 Decision trees and ensemble methods
 Gaussian processes
 Automatic differentiation
 Logistic regression
 Neural networks
 Unsupervised learning
 Deep generative models
 Other topics

Restriction 
The maximum number of students allowed on the module is 170. 
Teaching Method 
There will be formal lectures and lab classes. These classes may involve computerbased problem solving, examples and MCQ. The lectures and example classes will run concurrently, covering similar topics, with the theory being laid out in the lectures, and application of the theory taking place in example classes. 
Feedback 
Students will receive feedback on their
assignments through Blackboard and in the Lab Sessions. 