Complete course notes

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E d i t e d b y : M a r c o V a r r o n e M a c h i n e L e a r n i n g C o u r s e N o t e s These notes have been made thanks to the effort of Polimi Data Scientists staff. Are you interested in Data Science activities? Follow PoliMi Data Scientists on Facebook ! Polimi Data Scientist is a community of students and Alumni of Politecnico di Milano. We organize events and activities related to Artificial Intelligence and Machine Learning, our aim is to create a strong and passionate community about Data Science at Politecnico di Milano. Do you want to learn more? Visit our website and join our Telegram Group ! ! Credits The following notes have been written by the Polimi Data Scientists student association by combining Prof. Restelli’s lectures and slides with content from the following books: • Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006 • Sutton and Barto, “Reinforcement Learning: an Introduction”, MIT Press, 1998 • Mitchell, “Machine Learning”, McGraw Hill, 1997 1 Contents 1 Introduction: Machine Learning Models 4 1.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 I Supervised Learning 6 2 Introduction 7 2.1 Overview of Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3 Linear Regression 10 3.1 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Basis functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3 Direct approaches . . . . . . . . . . . . . . . . .…