Course Details
Course Information Package
Course Unit Title | MACHINE LEARNING | ||||||||
Course Unit Code | ACSC468 | ||||||||
Course Unit Details | |||||||||
Number of ECTS credits allocated | 5 | ||||||||
Learning Outcomes of the course unit | By the end of the course, the students should be able to:
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Mode of Delivery | Face-to-face | ||||||||
Prerequisites | NONE | Co-requisites | NONE | ||||||
Recommended optional program components | NONE | ||||||||
Course Contents | Introduction: Basic notions of learning; Unsupervised, semi-supervised and supervised learning; Goals and applications of machine learning; Aspects of developing a learning system: training data, concept representation, function approximation. Concept Learning: The concept learning task; Concept learning as search through a hypothesis space; General-to-specific ordering of hypotheses; Finding maximally specific hypotheses; Version spaces and the candidate elimination algorithm; Inductive bias and its importance.
Decision Tree Learning: Decision tree representation; The ID3 algorithm; Picking the best splitting attribute: entropy and information gain; Searching for simple trees and computational complexity; Occams razor; Overfitting, noisy data, and pruning; Continuous attributes and missing values.
Artificial Neural Networks: Neurons and biological motivation; Neural network representation; Perceptrons: representational limitation and gradient descent training; Multilayer networks; The backpropagation learning algorithm; Early stopping.
Instance-Based Learning: Induction versus transduction; The k-nearest neighbour algorithm; Locally weighted regression; Radial basis functions; Case-based reasoning.
Bayesian Learning: Basic concepts of probability theory; Bayes Theorem and MAP concept learning; Minimum description length principle; Bayes optimal classifier; Naive Bayes learning algorithm.
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Recommended and/or required reading: | |||||||||
Textbooks |
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References |
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Planned learning activities and teaching methods | The course is delivered through three hours of lectures per week, which include presentation of new material and demonstration of concepts and algorithms. Lectures also include in-class exercises to enhance the material learning process and to assess the student level of understanding and provide feedback accordingly. Furthermore a lot of work is in done through homework and private study by carrying out the computations of the different techniques for specific inputs and by experimenting in Matlab with the application of these techniques to benchmark datasets. This provides students with practical experience on the ideas and issues discussed in class. All lecture notes and other material is available to students through the course homepage. | ||||||||
Assessment methods and criteria |
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Language of instruction | English | ||||||||
Work placement(s) | NO |