CS 3400 - Machine Learning

3 lecture hours 2 lab hours 4 credits

Course Description
This course provides a broad introduction to machine learning. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions. Topic categories include optimization and both supervised and unsupervised methods. Students will reinforce their learning of machine learning algorithms with hands-on, tutorial-oriented laboratory exercises for development of representative applications. (prereq: MA 383 and (CS 2300 or CS 2852))

Course Learning Outcomes
Upon successful completion of this course, the student will be able to:

Understand the basic process of machine learning
Understand the concepts of learning theory, i.e., what is learnable, bias, variance, overfitting
Understand the concepts and application of supervised and unsupervised learning
Analyze and implement basic machine learning algorithms
Understand the role of optimization in machine learning
Assess the quality of predictions and inferences
Apply methods to real world data sets

Prerequisites by Topic

Linear algebra
Derivative calculus

Coordinator
Dr. John Bukowy

Announcement

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Milwaukee School of Engineering

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CS 3400 - Machine Learning