CSU-IMU-2023

Basic and advanced courses on Machine learning and Data Assimilation

Here you will find all the material for 2 courses that will be held at Caraga State University in October 2023. Partial funding was provided by the IMU's volunteer lecturer program.

Basic Course (30h)

In this course we present the basic theories of

• Machine Learning
• Data Assimilation

Advanced Course (30h)

In this course we present more advanced materail on Scientific Machine Learning that couple

• Machine Learning
• Computational Science

In particular, we will be interested in how to use ML approaches to solve direct and inverse problems, including DA problems.

Course prerequisites

• Basic Calculus and Linear Algebra
• Basic Statistics and Probability
• Scientific computing in Python

Software used in class

• A Python 3 distribution configured for scientific computing. The simplest way to set this up is by installing the Miniconda distribution, or the complete Anaconda distribution.
• Jupyter notebook. You will need this in order to follow some of the in-class tutorials.
• Pytorch. This is the sophisticated machine learning and deep learning package written in Python. Much of the latest machine learning research is done and published using PyTorch code.
• scikit-learn. Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection, model evaluation, and many other utilities. Built on NumPy, SciPy, and matplotlib.
• R. R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc.

Lecturer

Mark Asch is an Emeritus Professor of Mathematics at the University of Picardy Jules Verne, in Amiens France. His work spans a wide range of areas in computational science and engineering, and machine learning. Current research efforts include physics-informed machine learning, digital twins for green batteries, passive acoustic monitoring of endangered marine species.

• Email: mark.asch@u-picardie.fr

Course Website

Many more details, as well as links to the files in this github repository, can be found at:

CSU2023

LICENSES

• All text documents and lectures are licensed under CC-BY 4.0
• All code examples are licensed under the MIT License

This is specified in the corresponding directories.