Artificial intelligence and machine learning with applications

The details
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Undergraduate: Level 6
Monday 15 January 2024
Friday 22 March 2024
08 January 2024


Requisites for this module



Key module for

BA Q120 Linguistics with Data Science,
BA Q121 Linguistics with Data Science (Including Foundation Year),
BA Q122 Linguistics with Data Science (Including Placement Year),
BA Q123 Linguistics with Data Science (Including Year Abroad)

Module description

Artificial Intelligence is the science of making computers and machines to produce results and behave in a way that resembles human intelligence. This multidisciplinary activity involves the knowledge of different disciplines such as Computer Science, Mathematics and Statistics, but also includes important elements from Philosophy, Logic and even Psychology.

This module will provide you with a broad overview of Artificial Intelligence, as well as more detailed understanding of core concepts and models. We will follow an approach both theoretical and practical, describing the theory and fundamentals of machine learning models, as well as showing how to implement them and their applications.

Module aims

The aim of this module is:

  • To provide a general introduction to Artificial Intelligence and Machine Learning for students without a strong background in Mathematics or Computer Science.

Module learning outcomes

By the end of this module, students will be expected to have:

  1. A systematic understanding of key aspects of the nature of Artificial Intelligence, its scope and its limitations.

  2. The ability to apply underlying concepts and principles to formulate abstract problems in a way that AI techniques such as Searching Algorithms and Genetic Algorithms can solve them efficiently.

  3. The ability to apply underlying concepts and principles to use supervised machine algorithms, such as Random Forests and LASSO, for analysing datasets.

  4. A systematic understanding of key aspects of the functioning of Artificial Neural Networks and Convolutional Neural Networks, as well as use them in the context of computational linguistics.

  5. The ability to apply underlying concepts and principles to a range of AI algorithms for studying real world cases. This includes: determining which types of models are best suited considering the characteristics of the dataset studied; implementing in the code in Python/Jupyter; using appropriate testing procedures and benchmarks; correctly interpreting the outputs and limitations.

Module information

Nowadays, AI is well embedded in our society from self-driving cars to spam filters, and from finance trading to video games. All predictions state that more and more our society will depend on this technology with the consequent transformation of our society and our economy. The impact of AI affects any discipline and therefore it is important for everyone to understand its principles, applications and limitations. This course is suitable for any student regardless of their background.

We will start with the history of the AI and its general principles as well as current examples and challenges that AI is nowadays facing. Then we will review specific models and algorithms in order to provide a wide range of tools and the logic behind them. Finally, we will also put a strong accent on the actual application of those machine learning models by going through practicals and seminars that will teach how to use them in a Python environment.


  • Artificial Intelligence

    • History, definitions and Principles of AI.

    • Ethics of AI. Biases and its consequences.

    • Applications of AI.

  • Problem Solving

    • Searching Algorithms.

    • Genetic Algorithms.

  • Machine Learning

    • Supervised Learning, including LASSO and Random Forests.

    • Unsupervised Learning, including k-means Clustering.

    • Reinforced Learning.

  • Deep Learning

    • Artificial Neural Networks.

    • Training Artificial Neural Networks and backpropagation.

    • Convolutional Neural Networks and Image recognition.

Learning and teaching methods

Teaching in the School will be delivered using a range of face to face lectures, classes and lab sessions as appropriate for each module. Modules may also include online only sessions where it is advantageous, for example for pedagogical reasons, to do so.


The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Test    20% 
Coursework   Final Project  12/04/2024  80% 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Zoe Bartlett, email:
Dr Zoe Bartlett



External examiner

Dr Yinghui Wei
University of Plymouth
Available via Moodle
Of 54 hours, 48 (88.9%) hours available to students:
6 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.


Further information

Disclaimer: The University makes every effort to ensure that this information on its Module Directory is accurate and up-to-date. Exceptionally it can be necessary to make changes, for example to programmes, modules, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements, industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding. Changes to modules may for example consist of variations to the content and method of delivery or assessment of modules and other services, to discontinue modules and other services and to merge or combine modules. The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications and module directory.

The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.