DATA603
Principles of Machine Learning
Restriction: Must be in one of the following programs: (Data Science Post-Baccalaureate Certificate, Master of Professional Studies in Data Science and Analytics, or Master of Professional Studies in Machine Learning). Cross-listed with: MSML603. Credit only granted for: DATA603, MSML603 or CMSC643. Formerly: CMSC643. A broad introduction to machine learning and statistical pattern recognition. Topics include: Supervised learning: Bayes decision theory, discriminant functions, maximum likelihood estimation, nearest neighbor rule, linear discriminant analysis, support vector machines, neural networks, deep learning networks. Unsupervised learning: clustering, dimensionality reduction, PCA, auto-encoders. The course will also discuss recent applications of machine learning, such as computer vision, data mining, autonomous navigation, and speech recognition.
Fall 2024
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4.46
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2.93
Spring 2024
3 reviews
Average rating:
5.00
Past Semesters
4 reviews
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4.75
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N/A
17 reviews
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2.29
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4.46
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5.00
13 reviews
Average rating:
4.46
2 reviews
Average rating:
1.00