Dec 11, 2024  
2023-2024 Cal State East Bay Catalog 
    
2023-2024 Cal State East Bay Catalog [ARCHIVED CATALOG]

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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:
 

  1. Demonstrate proficiency in the concepts, techniques, and applications of machine learning
  2. Implement in code a variety of machine learning algorithms
  3. Understand the factors and tradeoffs involved when choosing between different machine learning approaches
  4. Apply machine learning algorithms to novel, real-world problems
  5. Identify the relevant ethical and social considerations inherent to machine learning practices




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