Students understand basic concepts and methods of machine learning. They can describe them in mathematical terms and can apply them to data using a high-level programming language (julia/python/R). ...
This course presents an introduction to statistical mechanics geared towards materials scientists. The concepts of macroscopic thermodynamics will be related to a microscopic picture and a statistical interpretation. Lectures and exercises will be compleme ...
This class discusses advanced data science and machine learning (ML) topics: Recommender Systems, Graph Analytics, and Deep Learning, Big Data, Data Clouds, APIs, Clustering. The course uses the Wolfram Language. Outcome: coding exercises in ML using rea ...
This class provides a hands-on introduction to data science and machine learning topics, exploring areas such as data acquisition and cleaning, regression, classification, clustering, neural networks, and visualization. The course consists of lectures and ...
To learn the basics of symbolic programming using Mathematica / To understand Mathematica expressions and their use / To be able to solve linear and non-linear differentials systems / To present graphically experimental or simulated results ...
The course will cover programming, numerical simulation, and visualization methods using Mathematica software. Students will be able to apply these skills to their currrent coursework, and prepared for the companion course (MSE 443(b)) which covers advanc ...