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  • ECE 284

    • THEORETICAL MACHINE LEARNING . ECE 284

    This course studies the mathematical foundations of machine learning, and focuses on understanding the trade-offs between statistical accuracy, scalability, and computation efficiency of distributed machine learning and optimization algorithms. Topics include empirical risk, convexity in learning, convergence analysis of gradient descent algorithm, stochastic gradient descent, neural networks, and reinforcement learning.

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    Prerequisites: ECE 235 or equivalent.

    4

    Units

    Letter

    Grading

    1, 2, 3

    Passtime

    None

    Level Limit

    Engineering

    College
    Instructors
    PEDARSANI R
    No info found
    Lecture
    PHELP1431
    T R
    14:00 PM - 15:50 PM
    30 / 30 Closed
    Enrollment Trends
    Grading Trends (4)
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    ECE 284 Pedarsani R Winter 2023 Total: 19
    ECE 284 Pedarsani R Winter 2022 Total: 19
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