Course CSE353
Title Machine Learning
Credits 3
Course Coordinator

Covers fundamental concepts for intelligent systems that autonomously learn to perform a task and improve with experience, including problem formulations (e.g., selecting input features and outputs) and learning frameworks (e.g., supervised vs. unsupervised), standard models, methods, computational tools, algorithms and modern techniques, as well as methodologies to evaluate learning ability and to automatically select optimal models. Applications to areas such as computer vision (e.g., character and digit recognition), natural-language processing (e.g., spam filtering) and robotics (e.g., navigating complex environments) will motivate the coursework and material.

Prerequisite Prerequisites: CSE 219 or CSE 260; CSE major
Pre- or Co-requisite: AMS 310 or AMS 311 or AMS 312
Course Outcomes
Major Topics Covered in Course
Laboratory Projects


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