Week 35: Introduction to the course, Logistics and Practicalities
Contents
Overview of first week
Reading Recommendations
Thursday August 26
Lectures and ComputerLab
Announcement
Communication channels
Course Format
Teachers
Deadlines for projects (tentative)
Recommended textbooks
Prerequisites
Learning outcomes
Topics covered in this course: Statistical analysis and optimization of data
Topics covered in this course: Machine Learning
Extremely useful tools, strongly recommended
Other courses on Data science and Machine Learning at UiO
Introduction
What is Machine Learning?
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
Basic Matrix Features
Some famous Matrices
More Basic Matrix Features
Numpy and arrays
Matrices in Python
Meet the Pandas
Friday August 27
Simple linear regression model using
scikit-learn
To our real data: nuclear binding energies. Brief reminder on masses and binding energies
Organizing our data
Seeing the wood for the trees
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
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
Some useful matrix and vector expressions
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 for week 36
Exercise 1: Setting up various Python environments
Exercise 2: making your own data and exploring scikit-learn
Exercise 3: Normalizing our data
Announcement
NORA AI competetion:
See the link here
https://www.nora.ai/Competition/image-segmentation.html
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