CISB 60  Machine Learning in Business

3.5 Units (Degree Applicable)
Lecture: 54   Lab: 27
Advisory: CISP 21 or CISP 71 or CISP 31 or CISP 41

A broad introduction to machine learning and its implementation to solve real-world business problems. Includes end-to-end process of investigating data through a machine learning lens and how to extract and identify useful features that best represent data and evaluate the performance of different machine learning algorithms. Topics include: supervised learning (linear regression, logistic regression, support vector machines, k-nearest neighbors, decision trees, random forest, and gradient boosted tree); unsupervised learning (clustering, dimensionality reduction, kernel methods); reinforcement learning and adaptive control.
Course Schedule

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