3 credits
Spring 2025 Lecture Upper Division
This course concerns the latest techniques in machine learning, focusing on deep learning and reinforcement learning, with applications to computer vision, natural language understanding, speech recognition, news recommendation, and others. The course plans to cover Neural Nets, Backpropagation, Convolutional neural network, Recurrent Networks, Autoencoders and other architechtures in Deep Learning in the rst half. Next, the course plans to cover multi-armed bandits, Contextual bandits, Markov decision process, and Policy gradient methods in Reinforcement Learning.
Learning Outcomes1Perceive the scope and importance of machine learning.
2Understand and evaluate modern machine learning algorithms.
3Apply appropriate programming skills to administer a machine learning algorithm in a real-world problem.
4Communicate the machine learning results in an appropriate level of detail for an intended audience.
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Attachments
made by Purdue students.