• Machine learning Basics - Capacity, Overfitting and Underfitting, types of errors, “No Free Lunch” Theorem, Regularization, Performance estimation - Bias, Variance, Holdout method (train\test split), Stratification, Model evaluation, Cross Validation, model and algorithm selection, Bootstrap methods
• Linear regression models, regularisation methods
• Linear mixed models
• Classification - different types of classifiers, Performance measures - precision, recall, f1, ROC and PR curve, imbalanced datasets,
• Statistical tests - which and when to use?, Hypothesis testing, p-value, Correlations, Permutations, Confidence intervals, Maximum likelihood
• Dimensionality reduction - curse of dimensionality, SVD, PCA (and variants such as IPCA, randomized and kPCA), LLE, LDA, t-SNE, autoencoders
• Data preprocessing - handling missing data, imputation, categorical data, scaling, feature importance, basic signal processing maybe
• Model evaluation and hyperparameter tuning - cross validation, grid and random search, learning curves, bias-variance tradeoff
• Clustering and other unsupervised methods, K-means, Hierarchical clustering
• Novelty and Outlier detection methods, RANSAC
• Analysis of variance - ANOVA, t-test, Kruskal-Wallis test
• Visualisations - best practices, packages
• Tree-based methods, random forests
• Ensemble learning - Bagging and Pasting, boosting, stacking, xgboost
• Neural networks Intro - basics, basic architectures, and when to use instead of classical methods
• Machine learning pipeline - steps, best practices, handling large datasets
• Reinforcement Learning - Intro
• Probabilistic graphical models - intro