Week 1: Basic Setup 
Week 2: Neural Networks 
Week 3: Approximation 
Week 4: Classical Approximations and the Universal Approximation Theorem (UAT) 
Week 5: Fourier Representations and the Barron Norm 
Week 6: Fourier Representations and the Barron Norm 
Week 7: Recent Advances in Neural Network Approximation 
Week 8: Recent Advances in Neural Network Approximation 
Week 9: Rademacher Complexity 
Week 10: Rademacher Complexity 
Week 11: Convergence Rate Analysis 
Week 12: Gradient Descent Theory 
Week 13: Gradient Descent Theory 
Week 14: Neural Tangent Kernel 
Week 15: Advanced Topics


강의계획서 내용인데 딥러닝 관련 수학 배우기 좋나?