CISB 60  Machine and Deep Learning in Business

3.5 Units (Degree Applicable)
Lecture: 54   Lab: 27
Prerequisite: CISD 41
Advisory: CISP 71

A broad introduction to machine learning and deep learning algorithms and their implementation to solve real-world business problems. Includes end-to-end process of investigating data through a machine learning lens and discuss how to extract and identify useful features that best represent your 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). Covers building deep learning prediction models of different complexities, from simple linear logistic regression to major categories of neural networks including convolutional neural networks (CNNs). Is structured around special coding blueprint approaches no mathematical complexities. The major goal of the course is to gain an immense amount of valuable hands-on experience with real-world business challenges.
Course Schedule

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