Machine Learning

Modeling & Data Analysis

Module 1. Introduction

- Philosophy
- Machine learning (ML)
- Need in Computational Biology
- Review of probability theory
- Basic concepts in machine learning

Module 2. Unsupervised and Reinforced Learning

- K-means and Gaussian Mixture Models
- K-Nearest Neighbors and Bayesian Classifiers
- Classification and Regression Trees (CART) & Random Forest
- Neural Networks

Module 3. Supervised and Reinforced Learning

- Regression
- Logistic Regression
- Support Vector Machines
- The kernel trick
- Neural Networks
- Monte Carlo inference
- Bayesian Inference

Module 4. Kernel methods in system identification

- Reproducing Kernel Hilbert Spaces
- Parametric model structures
- Regularized least squares method
- Selection of model flexibility: AIC, BIC, CV
- Marginal likelihood maximization
- Parameter estimation of biological models