Introduces students to computational intelligence and machine learning techniques including regression and artificial neural networks. The unit provides students with foundation knowledge and skills to utilize a range of computational intellligence and machine learning algorithms. Students will take an algorithmic approach to machine learning and learn through solving real-world problems.
Module 1: Introduction to machine learning
Module 2: Regression modelling
Module 3: Generalisation, model assessment and selection
Module 4: Feed-forward neural networks and backpropagation
Module 5: Convolutional neural networks
Module 6: Model regularisation
Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.
|On completion of this unit, students should be able to:|
|1||analyse and select machine learning solutions for real-world problems.|
|2||design and develop various machine learning based models.|
|3||evaluate and enhance the models to meet requirements.|
|4||interpret and communicate the results to stakeholders.|
On completion of this unit, students should be able to:
- analyse and select machine learning solutions for real-world problems.
- design and develop various machine learning based models.
- evaluate and enhance the models to meet requirements.
- interpret and communicate the results to stakeholders.
- Geron, A, 2019, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems , 2nd edn , O'Reilly.
Teaching and assessment
Commonwealth Supported courses
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