- Schedule of Classes - June 10, 2022 7:44AM EDT
- Course Catalog - June 9, 2022 7:14PM EDT
Course information provided by the Courses of Study 2021-2022.
The goal of this course is to teach you why machine learning works and how to implement it. We will cover the essentials of learning theory, including the probably approximately correct (PAC) framework and the bias-complexity tradeoff. We will then see how these concepts shed light on the mathematics behind linear regression, logistic regression, boosting (and AdaBoost), support vector machines and neural networks. We cover clustering algorithms and how to implement them. Data will be analyzed using modern software packages with the above algorithms, with the aim of reinforcing the mathematics behind them.
When Offered Spring.
Prerequisites/Corequisites Prerequisite: CS 1110 or equivalent, MATH 4710, STSCI 3080, STSCI 4030 or STSCI 5030. Recommended prerequisite: STSCI 4740.
- Students will be able to demonstrate an understanding of how concepts in learning theory quantify the performance of the learning algorithms in the course description.
- Students will be able to indicate a competency of how and in which circumstances to apply modern machine learning algorithms to real and simulated data.
- Students will be able to verify theoretical results—such as the Fundamental Theorem of Statistical Learning—in practice using the software packages introduced and taught in the course.
Regular Academic Session. Combined with: STSCI 4750
Credits and Grading Basis
4 Credits Stdnt Opt(Letter or S/U grades)
Class Number & Section Details
- MW Upson Hall 216
- Jan 24 - May 10, 2022
Instruction Mode: In Person
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