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- Intro of the course - 05/04/2024 - slides
- Lecture 1: Probability, Bayes theorem, random variables, probability density functions, expectation values, correlation and pdf transformations - 05/04/2024 - slides 1
2023 academic year
This course is structured in frontal lessons and practical example lessons using Python.
The syllabus of the course is available here.
The lecture calendar is available here.
The course material will be uploaded before each lesson here:
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- Lecture 2: Python intro, variables, functions, classes, numpy and pandas libraries, plotting - 12/04/2024 - slides 2 - notebooks: notebook_01, binder,
- Lecture 3: Pdf catalogue, Python SciPy library, Visualizing data, histograms - 19/04/2024 - slides 3 - notebooks: notebook_02
- Lecture 4a: Montecarlo method, random numbers with Python - 26/04/2024- slides 4 - notebooks: notebook_03
- Lecture 4b: Montecarlo method, random numbers with Python - 10/05/2024 - slides 4 - notebooks: notebook_03
- Lecture 5: Parameter estimation - 24/04/2024 - slides 5
- Lecture 6: Curve Fitting - 21/06/2024 - slides 6 - notebooks: notebook_04
- Lecture 7: Hypotesis test - 28/06/2024 - slides -
- Lecture 8: Classification - 12/07/2024 - slides -
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