COMP09012 2017 Machine Learning

General Details

Full Title
Machine Learning
Transcript Title
Machine Learning
Code
COMP09012
Attendance
N/A %
Subject Area
COMP - Computing
Department
MENG - Mech. and Electronic Eng.
Level
09 - NFQ Level 9
Credit
05 - 05 Credits
Duration
Semester
Fee
Start Term
2017 - Full Academic Year 2017-18
End Term
9999 - The End of Time
Author(s)
Sean Mullery
Programme Membership
SG_ECONN_M09 201800 Master of Engineering in Connected and Autonomous Vehicles SG_EAUTO_E09 201800 Certificate in Automotive Artificial Intelligence SG_ECONN_O09 201800 Postgraduate Diploma in Engineering in Connected and Autonomous Vehicles
Description

This module introduces the topic of machine learning algorithms (algorithms that learn from data), with the first part of the module dedicated to the standard shallow forms of machine learning before moving on to Deep Learning and Convolutional Neural Networks for use in computer vision tasks, particularly recognition, classification and localisation. The emerging topic of Deep Reinforcement Learning will be briefly introduced. The module will look at training strategies and frameworks for Deep Learning. As well as the technical/scientific elements, students will reflect on the ethical implications of machine learning.

Learning Outcomes

On completion of this module the learner will/should be able to;

1.

Compare state of the hand engineered detectors with machine learning techniques in terms of performance on appropriate metrics and data sets and determine the appropriateness of each for safety critical applications.

2.

Apply transfer learning to adapt a pre-trained network to a new classification problem.

3.

Assess the validity of various cost functions to specific machine learning problems.

4.

Effectively collaborate and communicate with others in the timely development of solutions to machine learning problems, including reports and software.

5.

Design, test and evaluate deep network architectures.

6.

Appreciate the data rights of citizens and the constraints these apply to the use of pattern detection in real world scenarios.

Teaching and Learning Strategies

A lecture will be provide each week. In advance of the lecture, the learner will be asked to review key text book chapters and academic papers that are relevant to the lecture so that they get the maximum learning from that lecture.

The Project work, both team and individual, will challege the learner to master concepts beyond those covered in the theory lecture. This will prepare them for the life long learning that will be required in the fast moving field of Computer Vision.

Module Assessment Strategies

A terminal exam and continuous assessment in the form of group project work will be used to assess the module.

To reinforce the theoretical principles covered in lectures, learners will participate in project work.

The learner will complete a final exam at the end of the semester.

The learner is required to pass both the projects and terminal examination element of this module.

 

Repeat Assessments

Repeat Exams will be set for Autumn of each year.

Repeat project work can be submitted at the repeat exam sitting.

Indicative Syllabus

Machine Learning: Logistic Regression, KNN, SVM, kernel SVM, Decision Trees.

Principle Component Analysis (PCA)

Model evaluation and Hyperparameter Tuning.

Deep Learning: Perceptron, multi-layer perceptron, representation learning.

Back propagation, SGD (variants), Cost/loss functions, regularisation.

Convolutional Neural Networks.

Dropout, Batch-normalisation.

Image classification, image recognition, localisation.

Transfer Learning.

Introduction to Reinforcement Learning.

Data Sets, ImageNet, Kaggle.

Data distributions, bias, variance, divergence (KL, JS, Wasserstein Distance).

Machine Learning Frameworks such as TensorFlow, Keras, PyTorch and Caffe.

Citizen rights under data protection, data storage.

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
60 %
End of Semester / Year Formal Exam
40 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Individual Project Project Individual Project 30 % Week 6 3,5
2 Group Project Project Group Project 30 % Week 12 2,3,4,5
             

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Terminal Exam Final Exam Assessment 40 % End of Semester 1,3,5,6
             
             

Full Time Mode Workload


Type Location Description Hours Frequency Avg Workload
Lecture Lecture Theatre Lecture 2 Weekly 2.00
Laboratory Practical Computer Laboratory Laboratory Practical 2 Fortnightly 1.00
Independent Learning Not Specified Independent Learning 7 Weekly 7.00
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Online Learning Mode Workload


Type Location Description Hours Frequency Avg Workload
Lecture Online Lecture 1 Weekly 1.00
Independent Learning Not Specified Independent Learning 8.5 Weekly 8.50
Laboratory Practical Online Online Lab 0.5 Weekly 0.50
Total Online Learning Average Weekly Learner Contact Time 1.50 Hours

Required & Recommended Book List

Recommended Reading
2017-01-03 Deep Learning (Adaptive Computation and Machine Learning Series) MIT Press
ISBN 0262035618 ISBN-13 9780262035613
Recommended Reading
2018-01-10 Deep Learning with Python Manning Publications
ISBN 1617294438 ISBN-13 9781617294433
Recommended Reading
2007-02-01 Pattern Recognition and Machine Learning (Information Science and Statistics) Springer
ISBN 0387310738 ISBN-13 9780387310732

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of...

Module Resources

Journal Resources

Proceedings of Neural Information Processing Systems https://papers.nips.cc

IEEE Transactions on Pattern Analysis and Machine Intelligence https://www.computer.org/web/tpami

arXiv Machine Learning https://arxiv.org/list/stat.ML/recent

International Journal of Computer Vision https://www.springer.com/computer/image+processing/journal/11263

 

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Other Resources

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