MA336-6-SP-CO:
Artificial intelligence and machine learning with applications
2021/22
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Spring
Undergraduate: Level 6
Current
Monday 17 January 2022
Friday 25 March 2022
15
16 June 2021
Requisites for this module
(none)
(none)
(none)
(none)
(none)
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)
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.
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.
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.
In this module we aim to provide a general introduction to Artificial Intelligence and Machine Learning for students without a strong background in Mathematics or Computer Science. 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.
On completion of the module students will have:
1. 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. 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:
A. Determining which types of models are best suited considering the characteristics of the dataset studied.
B. Implementing in the code in Python/Jupyter.
C. Using appropriate testing procedures and benchmarks.
D. Correctly interpreting the outputs and limitations.
1. Artificial Intelligence
1.1. History, definitions and Principles of AI.
1.2. Ethics of AI. Biases and its consequences.
1.3. Applications of AI.
2. Problem Solving
2.1. Searching Algorithms.
2.2. Genetic Algorithms.
3. Machine Learning
3.1. Supervised Learning, including LASSO and Random Forests.
3.2. Unsupervised Learning, including k-means Clustering.
3.3. Reinforced Learning.
4. Deep Learning
4.1. Artificial Neural Networks.
4.2. Training Artificial Neural Networks and backpropagation.
4.3. Convolutional Neural Networks and Image recognition.
The teaching methods used in this module consists in:
1. Lectures, where the theory will be taught.
2. Seminars, where the specific examples will be thoroughly reviewed including its implementation.
3. Labs, where the student will learn through hands-on exercises in a more interactive environment.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Online Test |
|
20% |
| Coursework |
Final Project |
|
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
Reassessment
Module supervisor and teaching staff
Dr Mario Gutierrez-Roig, email: mario.gutierrez-roig@essex.ac.uk.
Dr Mario Gutierrez-Roig & Dr Lisa Voigt
Dr Mario Gutierrez-Roig (mario.gutierrez-roig@essex.ac.uk), Dr Lisa Voigt (lv18675@essex.ac.uk)
Yes
Yes
Yes
Prof Fionn Murtagh
University of Huddersfield
Professor of Data Science
Dr Yinghui Wei
University of Plymouth
Available via Moodle
Of 950 hours, 12 (1.3%) hours available to students:
938 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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