Machine learning principles; The probabilistic perspective on machine learning; Supervised, unsupervised, and reinforcement learning; Biological neurons and their relation to linear classifiers; Supervised learning techniques: linear regression, the perceptron learning rule, backpropagation, recurrent networks, deep learning, support vector machines; Unsupervised techniques: k-means clustering, EM of gaussian mixtures, hidden markov models; Reinforcement learning: Policy-valu...
Learning Outcomes
Distinguish the different categories of Machine Learning techniques and identify situations in which they might be used
Describe the implementation of simple algorithms for machine learning
Discuss the adaptation of a standard technique to a given specific problem
Describe the implementation and evaluation some standard machine learning algorithms in a general purpose programming language such as Java
Obtain a basic understanding of machine learning approaches and neural networks
Discuss the relationships between training set, test set, generalisation, cross validation
Describe how various machine learning techniques work and what their strengths and limitations are
Select and use appropriate machine learning techniques to solve real problems