MACHINE LEARNING & NEURAL NETWORKS
- Module code: CS401
- Credits: 5
- Semester: 1
- Department: COMPUTER SCIENCE
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, backprogation, recurrent networks, deep learning, support vector machines; Unsupervised techniques: k-means clustering, EM of gaussian mixtures, hidden markov models; Reinforcement learning: Policy-value iteration, Q-learning, TD-learning, deep reinforcement learning systems.
On successful completion of the module, students should be able to:
|Teaching & Learning methods|
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