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.

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• An introductory understanding of the mathematical foundations of machine learning (e.g., vector space models) 
• Familiarity with basic, widely used machine learning tools (e.g., Weka, LibSVM) and algorithms (decision trees, logistic regressions, etc.).
• A working knowledge of the basic building blocks and design principles of machine learning with the ability to apply that knowledge to real data sets and evaluate their performance.

N/A

CSE353