MA336-7-SU-CO:
Artificial intelligence and machine learning with applications

The details
2023/24
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Summer
Postgraduate: Level 7
Current
Monday 22 April 2024
Friday 28 June 2024
15
08 January 2024

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

NONPYYMA Essex Abroad - Mathematics,
MSC G305JS Applied Data Science,
MSC G306JS Data Science and its Applications

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 Artificial Intelligence, its scope and its limitations.

  2. A comprehensive understanding of techniques applicable to formulate abstract problems in a way that AI techniques such as Searching Algorithms and Genetic Algorithms can solve them efficiently.

  3. A conceptual understanding that enables the student to use supervised and unsupervised machine algorithms, such as Decision Trees and K-Means Clustering, for analysing datasets.

  4. A systematic understanding of knowledge, and a critical awareness 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 a range of AI algorithms for studying real world cases. This includes:

    1. Determining which types of models are best suited considering the characteristics of the dataset studied.

    2. Implementing in the code in Python/Jupyter.

    3. Using appropriate testing procedures and benchmarks.

    4. Correctly interpreting the outputs and limitations.

    5. The ability to communicate effectively AI and Machine Learning concepts and ideas.



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.


Syllabus



  • 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.

Bibliography

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 & Presentation  26/06/2024  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%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Saideh Ferdowsi, email: s.ferdowsi@essex.ac.uk.
Dr Saideh Ferdowsi
s.ferdowsi@essex.ac.uk

 

Availability
Yes
Yes
Yes

External examiner

Dr Yinghui Wei
University of Plymouth
Resources
Available via Moodle
Of 397 hours, 0 (0%) hours available to students:
397 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information

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