Massih-Reza Amini is a professor of computer science at the university of Grenoble Alpes in France, and has worked in the field of machine learning for more than 20 years. He holds a chair in Machine Learning for Material Science at the Interdisciplinary Institute in Artificial Intelligence and is the head of the Machine Learning group at the Grenoble Computer Science Laboratory. In addition to co-authoring more than 160 scholarly articles, he has supervised more than 27 PhD students.

• 1. Fundamentals of Supervised Learning.- 2. Data-dependent generalization bounds.- 3. Descent direction optimization algorithms.- 4. Deep Learning.- 5. Support Vector Machines.- 6. Boosting.- 7. Semi-supervised Learning.- 8. Learning-To-Rank.- Appendix: Probability reminders.

Massih-Reza Amini is a professor of computer science at the university of Grenoble Alpes in France, and has worked in the field of machine learning for more than 20 years. He holds a chair in Machine Learning for Material Science at the Interdisciplinary Institute in Artificial Intelligence and is the head of the Machine Learning group at the Grenoble Computer Science Laboratory. In addition to co-authoring more than 160 scholarly articles, he has supervised more than 27 PhD students.

• 1. Fundamentals of Supervised Learning.- 2. Data-dependent generalization bounds.- 3. Descent direction optimization algorithms.- 4. Deep Learning.- 5. Support Vector Machines.- 6. Boosting.- 7. Semi-supervised Learning.- 8. Learning-To-Rank.- Appendix: Probability reminders.

Massih-Reza Amini is a professor of computer science at the university of Grenoble Alpes in France, and has worked in the field of machine learning for more than 20 years. He holds a chair in Machine Learning for Material Science at the Interdisciplinary Institute in Artificial Intelligence and is the head of the Machine Learning group at the Grenoble Computer Science Laboratory. In addition to co-authoring more than 160 scholarly articles, he has supervised more than 27 PhD students.

• 1. Fundamentals of Supervised Learning.- 2. Data-dependent generalization bounds.- 3. Descent direction optimization algorithms.- 4. Deep Learning.- 5. Support Vector Machines.- 6. Boosting.- 7. Semi-supervised Learning.- 8. Learning-To-Rank.- Appendix: Probability reminders.