This module provides a broad view of machine learning and neural networks. You will learn how to solve common machine learning problems such as regression, classification, clustering, matrix completion and pattern recognition. You will learn about neural networks and how they can be trained and optimised, including an exploration of deep neural networks. You will learn about machine learning and neural network software libraries that allow you to develop machine learning systems rapidly, and you will learn how to verify and evaluate the results.
- Dr. Jamie Ward
- Dr. Tim Blackwell
- Regression and classification
- Features and distances
- Supervised clustering
- Evaluation: accuracy, precision, recall and cross validation
- Dimensional reduction: principal component analysis
- Matrix completion
- Unsupervised clustering
- Multi-Layer Perceptrons and back progagation
- Network optimisers
- Deep and recurrent networks
Coursework only (Type II).
- At the time of this writing, you can apply for automatic RPL for this module if you obtain the IBM AI Engineering Professional Certificate.
- Deep Learning with Python (1st ed); Chollet, François (2017); Manning, New York.