ANALYSIS AND MACHINE LEARNING TECHNIQUES WITH BASICS OF COMPUTER SCIENCE AND STATISTICAL LEARNING - (CTF) Loriano Storchi)
Note the course is lectured in Italian.
These notes have no claim of completeness, but represent only a reference, I hope a useful one, for all those who have followed
the course. (Some unavoidable inaccuracies will be corrected as soon as possible)
Slides:
Warnings, slides may undergo changes during the course
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00:
Introduction
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01:
Introduction to Informatics
What is a computer ?, Networks and TCP/IP, Digitalization and basics of data encryption
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02:
Brief introduction to statistics,
Basic Concepts of Probability (The Language of Uncertainty)
Descriptive Statistics (Summarizing Data) and Inferential Statistics (Making Conclusions)
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03:
Brief introduction to programming languages and computer complexity
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04:
Python, Python data structures and more
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05:
Statistical Learning and Machine Learning,
ML Introduction, Unsupervised techniques, Reinforcement Learning,
Supervised Techniques, Regression and Classification,
LR and PLS and Logistic Regression and RF and GPR, Deep Learning, NN, CNN,
Interpretable ML, PySR: Python library for symbolic regression
Bibliography:
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"Computer organization and design : the hardware/software interface",
Book by David A Patterson, John L. Hennessy
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"TCP/IP Illustrated: Volume 1: The Protocols",
Book by W. Richard Stevens, Kevin R. Falls, Gary R. Wrigh
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"Introduction to Statistics:,
Book by David M. Lane1, David Scott, Mikki Hebl, Rudy Guerra, Dan Osherson, Heidi Zimmer
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"Introduction to Computer Programming with Python",
Book by Harris Wang
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"An Introduction to Statistical Learning: With Applications in Python",
Book by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor
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"The Hundred-Page Machine Learning",
Book by Andriy Burkov
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"nterpretable Machine Learning: A Guide for Making Black Box Models Explainable",
Book by Christoph Molnar
