CS457 - Machine Learning
| TIMETABLE | TEACHING MATERIAL |
| Credits | 20 |
| Level | 4 |
| Semester | Term 3 |
| Availability | Available to participants taking UG Graduate and Degree Apprenticeship programmes, e.g. BSc Hons IT: Software Development. |
| Prerequisites | CS353 Fundamentals of Data Analytics |
| Learning Activities Breakdown | 12 tutorials, online study and preparation for the class test and coursework assignment. |
| Assessment | The class will be assessed 100% by coursework which will consist of two assignments worth 50% each. |
| Lecturer | Joseph El Gemayel |
Aims and Objectives
The aim of this class is to equip participants with a sound understanding of the principles of Machine Learning and a range of popular approaches, along with the knowledge of how and when to apply the techniques. The class balances a solid theoretical knowledge of the techniques with practical application via Python (and associated libraries).
Learning Outcomes
After completing this class participants will be able to:
- Understand the aims and fundamental principles of Machine Learning.
- Understand a range of the essential core algorithms and approaches to Machine Learning.
- Apply the algorithms covered on substantial data sets using Python and Scikit-learn, and interpret the outcomes.
- Understand the applicability of these algorithms to different types of data and problems along with their strengths and limitations.
- Understand the limitations of basic approaches to Deep Learning and the need for advanced strategies.
- Understand and apply a range of the advanced algorithms and approaches to Deep Learning and Machine Learning using Artificial Neural Networks and interpret the outcomes.
Syllabus
Indicative topics:
- Machine learning basics
- Overview of the machine learning process
- Classifiers and classification measures
- Training models
- Support Vector Machines
- Decision trees
- Ensemble Learning
- Dimensionality Reduction (Principal Components Analysis)
- Introduction to Neural Networks
- Perceptrons and Artificial Neural Networks
- Backpropagation and Network Training
- Multi-layer and Deep Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
Recommended Reading
This list is indicative only – the class lecturer may recommend alternative reading material. Please do not purchase any of the reading material listed below until you have confirmed with the class lecturer that it will be used for this class.
Tensorflow Tutorials at: http://www.tensorflow.org/overview/ Neural Networks and Deep Learning at: http://neuralnetworksanddeeplearning.com/
Last updated: 2022-12-15 14:52:05