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Dec 04, 2024
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CSCI 5770G - Machine Learning
This course will focus on both supervised and unsupervised learning methods, covering both theory and practice. The course is geared towards students who wish to develop a working knowledge of the recent advances in machine learning, and how these are applied in various domains. Machine learning deals with how to design computer programs that learn from “experience.” Residing at the intersection of computer science and statistics, machine learning aims to extract useful information from data (often referred to as the training data) and leverages this information to create computer models capable of carrying out useful, non-trivial tasks, such as designing cars that can drive on their own, filters for blocking junk email, diagnostics tools for disease discovery, etc. Students are expected to have prior background in linear algebra, calculus, probability and statistics, as well as experience in a high-level scripting language such as Python or MATLAB.
The course will cover the following topics:
- Calculus, linear algebra and statistical foundations of machine learning
- Non-parametric learning
- Linear regression, Maximum Likelihood Estimation view of least regression
- Logistic regression, softmax
- Feed-forward perceptron networks
- Autoencoders, Siamese networks
- Recurrent neural networks, long short-term memory networks
- Generative adversarial learning
- Gaussian processes
- Reinforcement learning
- Bayesian learning
Credit hours: 3
Credit restriction(s): MITS 6800G
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