Course 2022-2023 a.y.

• Introduction to the main Machine Learning problems and frameworks.
• Supervised learning: linear regression, logistic regression, generative learning algorithms, kernel methods, SVM, decision trees
• Neural Networks and backpropagation
• Bias-variance tradeoff, model selection
• Unsupervised learning: Principal Component Analysis, hierarchical clustering, k-means
• Basics of Reinforcement learning
• Implementing Machine Learning Pipelines with sklearn

• State and use the basic results of statistical inference for data analysis.
• Distinguish and characterize the following basic machine learning problems: regression, classification, clustering, dimensionality reduction.
• Describe the basic conceptual ideas, strengths, and limitations of the different learning algorithms for the settings above.
• Evaluate the main trade-offs such as overfitting, and computational cost vs accuracy

• Implement end-to-end machine learning pipelines and rigorously evaluate their performance.
• Demonstrate the ability to cope with real-world data analysis and problem solving (managing, preprocessing and analyze real datasets).

• Face-to-face lectures
• Exercises (exercises, database, software etc.)
• Individual assignments

• Face-to-face lectures focus on the theoretical, methodological, and computational aspects of the topics covered by the course.
• In hands-on exercise sessions students will work on their laptops to implement solutions to relevant case studies. Students will use Python and common libraries such as scikit-learn.
• Individual assignments: each student is required to solve a machine learning problem and provide a written report and the related code.

The exam consists of a theory part and a problem solving project.

• The theory part consists in exercises and questions to be answered on paper, and is used to asses the "knowledge and understanding" learning   objectives. This contributes to 50% of the final grade.
• The project consists in solving a practical ML problem, to be developed individually and described through a written report, which is evaluated by the instructors. This contributes to 50% of the final grade.

The individual project is used to asses the "applying knowledge and understanding" learning objectives. In order to pass the exam, students must achieve a passing grade in both the theory part and the project part.

• C.M. BISHOP, Pattern Recognition and Machine Learning, Springer, 2006.
• T. HASTIE, R. TIBSHIRANI, J. FRIEDMAN, The Elements of Statistical Learning, Springer, 2009. 
• K.P. Murphy, Machine learning: a probabilistic perspective, 2012.
• R.S. Sutton, Richard, A. G. Barto,  Reinforcement learning: An introduction, 2018.