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ITCS 6156 - Machine Learning
Machine learning has been successfully applied to many different areas such as autonomous control of cars and robots, natural language processing, image recognition, health science, biology, and data mining. This course introduces fundamental concepts and methods to learn from data for computational data analysis, including pattern recognition, prediction, and visualization. For this, supervised learning, unsupervised learning, and reinforcement learning, as well as techniques including clustering, classification, support vector machines, and neural networks are covered. 3.000 Credit hours 3.000 Lecture hours Levels: Graduate Schedule Types: Lecture Computer Science Department Restrictions: Must be enrolled in one of the following Levels: Graduate Prerequisites:

ITCS 6156 - Machine Learning

Machine learning has been successfully applied to many different areas such as autonomous control of cars and robots, natural language processing, image recognition, health science, biology, and data mining. This course introduces fundamental concepts and methods to learn from data for computational data analysis, including pattern recognition, prediction, and visualization. For this, supervised learning, unsupervised learning, and reinforcement learning, as well as techniques including clustering, classification, support vector machines, and neural networks are covered.
3.000 Credit hours
3.000 Lecture hours

Levels: Graduate
Schedule Types: Lecture

Computer Science Department

Restrictions:
Must be enrolled in one of the following Levels:
Graduate

Prerequisites:

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Detailed Course Information

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