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.
Ramtin Pedarsani
4.0