MA336-7-SU-CO:
Artificial intelligence and machine learning with applications
2020/21
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Summer
Postgraduate: Level 7
Current
Monday 26 April 2021
Friday 02 July 2021
15
21 April 2021
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G305JS Applied Data Science
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 proceed to 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 seminars that will teach how to use them in an R environment.
A. A comprehensive knowledge and familiarity in understanding the nature of Artificial Intelligence, its scope and its limitations.
B. A comprehensive knowledge and familiarity with knowing the current areas of application of AI and the most iconic examples as well as identifying in which areas where AI could potentially provide solutions.
C. A comprehensive knowledge and familiarity with distinguishing the different types of machine algorithms, understanding their logic and knowing examples of its application.
D. A comprehensive knowledge and familiarity with the ability to use AI for analysing real world cases. This includes:
a. Determine which types of models are best suited considering the characteristics of the dataset studied
b. Implementing in R a range of machine learning algorithms
c. Using appropriate testing procedures
d. Correctly interpreting the output and limitations.
1. Artificial Intelligence
1.1. History of AI
1.2. Principles of AI
1.3. Philosophy and Ethics of AI
1.4. Biases and its consequences on AI
2. Problem Solving
2.1. Games, Formulations and Strategies
2.2. Genetic Algorithms
2.3. Bayesian Networks
3. Machine Learning
3.1. Supervised Learning
3.2. Unsupervised Learning
3.3. Reinforced Learning
4. Deep Learning
4.1. Artificial Neural Networks
4.2. Training algorithms and backpropagation
4.3. Convolutional Neural Networks
4.4. Computer Vision and Speech Recognition
The teaching methods used in this module consists in:
Lectures, where the theory will be taught.
Seminars, where the specific examples will be thoroughly reviewed including its implementation.
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.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Online Test |
|
20% |
| Coursework |
Final Project & Presentation |
|
60% |
| Coursework |
Final Project: Presentation |
|
20% |
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
Available via Moodle
Of 414 hours, 0 (0%) hours available to students:
414 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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