Machine Learning (CS4256.01)

Justin Vasselli

In the course of our daily lives we interact with many systems that have been trained to perform their jobs not based on meticulously designed domain-specific algorithms, but instead based on large amounts of data.  This is the foundation of Machine Learning. Today, everything from auto-complete to spam-filtering is done using machine learning techniques.  This course will be a broad introduction to the concepts and algorithms that allow machines to learn from data and improve performance through experience.  We will look at supervised learning algorithms such as logistic regression, classification, and neural networks, as well as unsupervised learning algorithms such as clustering. We will explore applications of these algorithms to problems such as recommendation engines and sentiment analysis.

This course will go into some advanced mathematics, so it is expected that you have taken Linear Algebra and Probability, and are enrolled in Advanced Linear Algebra this term.

Learning Outcomes:

Delivery Method: Entirely remote (synchronous)
Prerequisites:Linear Algebra, Probability.
Corequisites: Advanced Linear Algebra.
Course Level: 4000-level
Credits: 4
T/F 10:30AM - 12:20PM (Full-term)
Maximum Enrollment: 14
Course Frequency: One time only

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