Machine Learning in Biomedicine
- Introduction to basic machine learning concepts
- Introduction to supervised and unsupervised learning algorithms along with examples of them in medicine
- Data preparation for machine learning
- Data visualization
- Introduction to dimensionality reduction and feature selection methods
- Filter based feature selection methods
- Wrapper based feature selection methods
- Model based feature selection methods
- Dimensionality reduction methods (PCA, etc.)
- Introduction and implementation of various classification, regression and clustering methods
- Regression methods (Linear, Polynomial, Logistic Regression)
- k-nearest neighbors algorithm
- Support Vector Machine
- Naive Bayes
- linear discriminant analysis
- Tree classifier
- Ensemble Classifier
- K-means
- Methods of evaluating and validating results
- Familiarity and introduction of other existing methods (without going into details) (Bayesian networks, hidden Markov models, shallow neural networks, deep learning, reinforcement learning)