CE802-7-AU-CO:
Machine Learning and Data Mining
2020/21
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 08 October 2020
Friday 18 December 2020
15
08 April 2021
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G41112 Artificial Intelligence,
MSC G51512 Big Data and Text Analytics,
MSC G40812 Intelligent Systems and Robotics,
MSC G30412 Data Science,
MSC G30424 Data Science,
MSC G304PP Data Science with Professional Placement,
MSC G41212 Artificial Intelligence and its Applications,
MSC G30612 Data Science and its Applications,
MPHDG30448 Data Science,
PHD G30448 Data Science,
MSCIN399 Actuarial Science and Data Science,
MSCIG199 Mathematics and Data Science
The module assumes a reasonable programming background and is not suitable for students without prior programming experience.
This module provides an understanding of machine learning, the methods involved in evaluating them, and their application to real-world problems. It will include classification and regression learning along with other techniques, and apply the techniques to particular classes of problems.
The aim of this module is to provide an understanding of the major approaches to machine learning, the methods involved in evaluating them, and their application to the solution of real problems.
On completion of the course, students should be able to:
1. demonstrate an understanding of the major approaches to classification and regression learning
2. demonstrate an understanding of other machine learning techniques that have important practical applications
3. identify machine learning techniques appropriate for particular classes of problem and apply them to practical problems
4. undertake a comparative evaluation of several machine learning procedures
The module assumes a reasonable programming background and is not suitable for students without prior programming experience.
Syllabus
Introduction:
What is meant by machine learning
Taxonomy of machine learning algorithms
The inductive bias
Data mining
Learning to classify:
Decision tree induction
Naïve Bayes methods
Bayesian networks
K-nearest neighbour method
Support vector machines
Learning to predict numeric values:
Linear Regression
Regression trees
Evaluating learning procedures
Overfitting and the 'bias-variance trade-off'
Applications of machine learning
Clustering:
k-means algorithm
agglomerative hierarchical methods
Association rules mining:
A priori algorithm
Reinforcement learning:
Q learning
Multiple learners:
Bagging, boosting, forests and stacking
Lectures, Labs and Classes.
- Alpaydin, Ethem. (©2014) Introduction to machine learning, Cambridge, Massachusetts: The MIT Press.
- Geron, Aurelien. (2019) Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, Sebastopol: O'Reilly Media, Inc, USA.
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 |
Progress Test 1 - Week 8 |
|
25% |
| Coursework |
Progress Test 2 - Week 11 |
|
25% |
| Coursework |
Assignment 1 - Report on Practical Exercise |
|
50% |
| Exam |
Reassessment Main exam: 180 minutes during Summer (Main Period)
|
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
Prof Luca Citi, email: lciti@essex.ac.uk.
Dr Luca Citi, Dr Sebastian Halder
School Office, e-mail csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770.
Yes
No
No
Dr Robert Mark Stevenson
University of Sheffield
Senior Lecturer
Available via Moodle
Of 1111 hours, 0 (0%) hours available to students:
1111 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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