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Dec 11, 2024
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CS 667 - Machine Learning Units: 3 Introduction to machine learning with an emphasis on the underlying mathematics. Topics such as VC dimension, central limit theorem, gradient descent. Applications such as SVM and neural nets.
Prerequisites: MATH 230 and MATH 310; or M.S. Computer Science major. Possible Instructional Methods: On-ground or Hybrid. Grading: A-F grading only. Course Typically Offered: Fall & Spring
Student Learning Outcomes - Upon successful completion of this course students will be able to:
- Demonstrate proficiency in the concepts, techniques, and applications of machine learning
- Implement in code a variety of machine learning algorithms
- Understand the factors and tradeoffs involved when choosing between different machine learning approaches
- Apply machine learning algorithms to novel, real-world problems
- Identify the relevant ethical and social considerations inherent to machine learning practices
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