Course Overview
Advanced study of deep learning theory and practice, focusing on neural network-based approaches and current research topics.
Foundational Principles
- Machine learning fundamentals
- Neural network basics
- Backpropagation theory
- Optimization in deep learning
- Regularization techniques
Modern Deep Learning
- Deep neural network architectures
- Convolutional neural networks
- Recurrent neural networks
- Transformer architectures
- Attention mechanisms
- Neural network scaling laws
Advanced Topics
- Representation learning
- Deep generative models
- Self-supervised learning
- Transfer learning
Current Research Areas
- Baseball pitch prediction software
- Human eye ultrasonography analysis