Mathematics and Applications of Machine Learning¶
Version: 2017-01-31
Contents:
- 1. Mathematics and Applications of Machine Learning
- 2. Introduction
- 3. First steps: Linear Classification
- 4. Optimization theory
- 5. Nonlinear classification problems
- 6. Neural networks
- 7. Representation and approximation by neural networks
- 8. Outlook
- 9. References
Possible choices of topics to proceed with:
Introduction into the mathematics of neural networks
- Universal approximation theorem
- Complexity analysis
Benchmark: support vector machines
- Linear classification
- Non-linear classification
Recurrent neural networks
- Learning addition with carry
- Pattern recognition in texts
Reinforcement learning