Applied Machine Learning and Data Analysis
Introduction to Applied Data Analysis and Machine Learning
Supervised Learning
1. Elements of Probability Theory and Statistical Data Analysis
2. Getting started, our first data and Machine Learning encounters
3. Linear Regression and more Advanced Regression Analysis
4. Logistic Regression
5. Neural networks, from the simple perceptron to deep learning
6. Support Vector Machines, overarching aims
7. Dimensionality Reduction
8. Convolutional Neural Networks
9. Recurrent Neural Networks
10. Solving ODEs with Deep Learning
11. Data Analysis and Machine Learning:
12. Elements of Bayesian theory and Bayesian Neural Networks
Index
