Student should be able to understand and describe::

• The concept of learning from data in contrast to rule-based systems.
• The differences between the various machine learning paradigms (supervised learning, unsupervised learning, reinforcement learning, active learning).
• The statistical foundations of machine learning (predictor, loss function, statistical risk, Bayes optimal prediction, Bayes risk, bias-variance decomposition, overfitting and underfitting, consistency, regularization, stability, model validation and model selection).
• The basic linear models for regression and classification (linear regression and linear classification, logistic regression, Support Vector Machines).
• The theory of kernel functions and the implementation of linear models in kernel spaces.
• The notion of nonparametric learning, its connection to consistency, the curse of dimensionality, and the basic nonparametric algorithms (e.g., k-NN, tree predictors, universal kernels).
• Probabilistic models (e.g., Naive Bayes classifiers, graphical models, Gaussian mixture models).
• Ensemble learning methods (bagging, boosting, stacking) and their effect on the bias-variance dilemma.
• Feed-forward neural networks, expressivity versus network size, hardness of training, backpropagation algorithm.
• Basics of online learning and reinforcement learning (e.g., online gradient descent, multi-armed bandits, Markov decision processes with finite and discounted horizon, model-based and model-free RL algorithms).
• Basic clustering algorithms (e.g., k-means and k-means++, DBSCAN, hierarchical clustering, correlation clustering).
• Model validation and selection (e.g., cross-validation, nested cross-validation).
• Basic techniques for dimensionality reduction (e.g., LDA, PCS, ISOMAP, LLE).

• Understand the correct methodological approach for implementing learning algorithms and running experiments, including model validation and selection, and reproducibility.
• Understand the basic techniques for hyperparameter tuning and their correct implementation.
• Know the basic evaluation metrics and the basic techniques for pre-processing the datasets, including feature selection, normalization, and transformation (e.g., for handling categorical features).

• Work effectively with others in an interdisciplinary and/or international team.
• Design and manage individual projects.
• Clearly and succinctly communicate their ideas to technical audiences.