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- Intro of the course - 27/03/2023 - slides
- Lecture 1: Probability, Bayes theorem, random variables, probability density functions, expectation values, correlation and pdf transformations - 27/03/2023 - slides 1
- Lecture 2: pdf catalogue, Montecarlo method - 31/03/2023 - slides 2a, slides 2b
- Lecture 3: Python intro, variables, functions, classes, numpy and pandas libraries, plotting - 03/04/2023 (recording) - notebooks: nbviewer, binder, .zip
- Lecture 4: Generating random variables with Python - 14/04/2023 (recording) - notebooks: nbviewer, binder, .zip
- Lecture 5: Parameter estimation and Maximum Likelihood Estimators - 17/04/2023 - slides 5
- Lecture 6: Confidence intervals, Curve fit, Least square method - 21/04/2023 - slide 6a, slide 6b
- Lectures 7/8: ML and LS fit with python -Hypotesis test and pvalue - 15/05/2023 - slide 8, notebooks: nbviewer, .zip
- Lecture 9: Multivariate analysis and classification tasks, 22/05/2023 (recording) - slide 9
- Lecture 10: Introduction to Machine Learning with Python, 29/05/2023 - notebooks: nbviewer, .zip
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