Week 34: Introduction to the course, Logistics and Practicalities
Contents
Overview of first week
Schedule first week
Lectures and ComputerLab
Communication channels
Course Format
Teachers
Deadlines for projects (tentative)
Grading
Reading material
Main textbooks
Other popular texts
Reading suggestions week 34
Prerequisites
Topics covered in this course: Statistical analysis and optimization of data
Statistical analysis and optimization of data
Machine Learning
Deep learning methods
Extremely useful tools, strongly recommended
Other courses on Data science and Machine Learning at UiO
Other courses on Data science and Machine Learning at UiO, contn
Learning outcomes
Types of Machine Learning
Essential elements of ML
An optimization/minimization problem
A Frequentist approach to data analysis
What is a good model?
What is a good model? Can we define it?
Software and needed installations
Python installers
Useful Python libraries
Installing R, C++, cython or Julia
Installing R, C++, cython, Numba etc
Numpy examples and Important Matrix and vector handling packages
Numpy and arrays
Matrices in Python
Meet the Pandas
Pandas AI
Simple linear regression model using
scikit-learn
To our real data: nuclear binding energies. Brief reminder on masses and binding energies
Organizing our data
And what about using neural networks?
A first summary
Why Linear Regression (aka Ordinary Least Squares and family)
Regression analysis, overarching aims
Regression analysis, overarching aims II
Examples
General linear models and linear algebra
Rewriting the fitting procedure as a linear algebra problem
Rewriting the fitting procedure as a linear algebra problem, more details
Generalizing the fitting procedure as a linear algebra problem
Generalizing the fitting procedure as a linear algebra problem
Optimizing our parameters
Our model for the nuclear binding energies
Optimizing our parameters, more details
Interpretations and optimizing our parameters
Interpretations and optimizing our parameters
Interpretations and optimizing our parameters
Own code for Ordinary Least Squares
Adding error analysis and training set up
The \( \chi^2 \) function
The \( \chi^2 \) function
The \( \chi^2 \) function
The \( \chi^2 \) function
The \( \chi^2 \) function
The \( \chi^2 \) function
Fitting an Equation of State for Dense Nuclear Matter
The code
Splitting our Data in Training and Test data
Exercises
Exercise 1: Setting up various Python environments
Exercise 2: making your own data and exploring scikit-learn
Exercise 3: Split data in test and training data
Pandas AI
Try out
Pandas AI
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