Introduction to basic machine learning concepts and algorithms including theoretical foundations, analysis and practical applications. The course provides understanding of the relevant supervised and unsupervised learning approaches and offers best practices and operational advises on their implementation.

Students will be able to identify and exemplify machine learning problems. They will be able to interpret and analyze machine learning algorithms, implement them (in Python), and evaluate algorithms' performance. Students will know how to combine the algorithms and compose workflows from data preprocessing to performance validation step. Gaining experience on how to overcome common implementation problems (accuracy, computational cost, overfitting, regularization).

Introduction and basic concepts. Machine learning system's components. Learning types. Approaches to ML. Different machine learning problems. Fundamental concepts: cost-functions, overfitting, regularization, cross-validation, bias-variance trade-off, curse of dimensionality. Supervised learning (Bayesian decision theory; quadratic classifiers; density estimation: parametric (Maximum Likelihood and Bayesian estimation) and non-parametric (kernel density estimation, kNN); linear and logistic regression; linear discriminative functions; neural networks; support vector machines) Unsupervised learning (k-Means Clustering; Hierarchical clustering) Dimensionality reduction: PCA , LDA.

Lectures, computer lab sessions (Python and other appropriate programming envionments), homework, consultations, active learning, project and research based learning, workshops.