2023 academic year
These lectures are part of the training offer (link) of the PhD course in Science of Matter and Nanomaterials at the University of Roma Tre.
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:
- 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 - notebooks: nbviewer, binder, .zip
- Lecture 4: Generating random variables with Python - 14/04/2023 - 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 - slide 9
- Lecture 10: Introduction to Machine Learning with Python, 29/05/2023 - notebooks: nbviewer, .zip