Machine Learning Theory
ECE 434/COS 434
1222
1222
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The course covers basic theories of modern machine learning: 1. statistical learning theory: generalization, uniform convergence, Rademacher complexity, VC theory, reproducing Hilbert kernel space and their applications on simple classification/regression models; 2. optimization theory: gradient descent, stochastic gradient descent and their convergence analyses for convex functions, nonconvex functions 3. deep learning theory: basic approximation, optimization and generalization results for deep neural networks; 4. reinforcement learning theory: MDP, Bellman equations, planning, and sample complexity results for value iteration/Q-learning.
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Section L01
- Type: Lecture
- Section: L01
- Status: O
- Enrollment: 30
- Capacity: 60
- Class Number: 22856
- Schedule: MWF 09:00 AM-09:50 AM - Engineering Quad D-Wing D221