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STAT41120

Academic Year 2025/2026

Machine Learning and AI (STAT41120)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Michael Fop
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module delves into advanced machine learning techniques central to modern artificial intelligence. Building upon foundational knowledge from "STAT30270 Statistical Machine Learning", students will explore advanced methods and models that enable accurate prediction and the ability to learn from complex, high-dimensional data.

Key topics include (subject to changes):
- Deep learning - Understanding and implementing neural networks, including convolutional and recurrent architectures, for processing data such as images, text, and time series.
- High-dimensional data - Techniques for managing and extracting insights from large-scale datasets, with an emphasis on scalability and computational efficiency.
- Model evaluation and benchmarking - Approaches to assess and compare predictive models to ensure robustness, generalizability, and performance.
- Interpretability and uncertainty - Exploring the 'black-box' nature of complex models, including methods for model interpretation and quantifying predictive uncertainty.

Practical sessions will involve hands-on experience with the R programming language and the Keras library, facilitating the application of concepts to real-world problems. By the end of the module, students will be equipped to design, implement, and critically evaluate advanced machine learning methods across various domains.

About this Module

Learning Outcomes:

On completion of this module, students should have acquired the following skills:
- Have an understanding of the theory and principles behind advanced machine learning techniques.
- Apply appropriately machine learning and artificial intelligence methods to complex, high-dimensional datasets.
- Implement tune, evaluate, and benchmark predictive models using appropriate approaches, metrics and validation strategies.
- Interpret the results of advanced machine learning and artificial intelligence techniques, recognizing the limitations of interpretability and identifying approaches for assessing uncertainty.
- Use software (R and keras) to implement advanced machine learning models and methods.

Indicative Module Content:

Indicative content (subject to changes):
- Foundations of machine learning and AI
- High-dimensional data
- Deep learning and advanced machine learning methods
- Model evaluation and benchmarking
- Interpretability and uncertainty
- Advanced topics

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Tutorial

6
Specified Learning Activities

25
Autonomous Student Learning

65
Total

120

Approaches to Teaching and Learning:
Lectures, tutorials, computer labs, enquiry and problem-based learning.

Requirements, Exclusions and Recommendations
Learning Requirements:

- Knowledge and understanding of statistical machine learning theory and methods for supervised learning and classification, at a level equivalent to that which would be achieved upon completion of "STAT30270 Statistical Machine Learning" (or STAT40750), or modules with similar contents and learning outcomes.
- Knowledge of data programming and data analysis at a level equivalent to that which would be achieved upon completion of "STAT20250 Data Programming with R", and modules with a relevant component of coding and implementation of statistical methods with R.
- Knowledge of linear algebra (vectors, vector spaces, matrices), calculus (derivatives), and function optimization.
- Knowledge of regression analysis and linear models, including multiple linear regression.
- Understanding of statistical inference (confidence intervals, hypothesis testing, etc.) and familiarity with standard probability distributions (Gaussian, binomial, etc.).

Learning Recommendations:

- Knowledge and understanding of basic Bayesian inference.
- Familiarity with data programming in Python.


Module Requisites and Incompatibles
Incompatibles:
STAT40970 - Machine Learning & AI (online)


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): The assignment may include a mix of exercises, questions, code-based exercises, data analysis tasks. Week 11 Other No
30
 No
Exam (In-person): End of term exam. End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
70
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Goodfellow, Bengio, Courville - Deep Learning - https://www.deeplearningbook.org

Hastie, Tibshirani, Friedman - The Elements of Statistical Learning - https://hastie.su.domains/ElemStatLearn/

Molnar - Interpretable Machine Learning - https://christophm.github.io/interpretable-ml-book

Murphy - Probabilistic Machine Learning: An Introduction - https://probml.github.io/pml-book/book1.html

Murphy - Probabilistic Machine Learning: Advanced Topics - https://probml.github.io/pml-book/book2.html

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 15:00 - 15:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 10:00 - 10:50
Spring Tutorial Offering 1 Week(s) - 22, 24, 26, 29, 30, 32, 33 Fri 09:00 - 09:50
Spring Tutorial Offering 1 Week(s) - 23, 25, 29, 31, 32, 33 Thurs 09:00 - 09:50
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