MAT388E-Data Analysis in Fundamental Sciences

Course Instructor: Gül İnan

Course Summary:

MAT388E is an undergraduate level course which aims to provide an introduction to commonly used statistical methods for inference and prediction problems in data analysis. This course is designed such that:

• The methods covered will include supervised learning algorithms with a focus on regression and classification problems and unsupervised learning algorithms with a focus on clustering problems,
• Application of these methods to data analysis problems and their software implementation will be done via Python.

At the end of the semester, the students are expected:

• To be fluent in the fundamental principles behind several statistical methods,
• To be able to apply statistical methods to real life problems and data sets, and
• To be prepared for more advanced coursework or industrial internship in machine learning and related fields.

Course GitHub Organization: https://github.com/MAT388E-Spring23 for GitHub classroom.

Course Prerequisites:

Since the course also touches on the mathematical and statistical theory behind the methods and uses Python for implementation, this course requires the following background:

• Knowledge of linear algebra, probability, statistics, and optimization,
• Familiarity with Python’s Numpy, Pandas, Matplotlib, Seaborn, Statsmodels, and Scikit-Learn libraries,
• Familiarity with at least one computational document such as Jupyter Notebook, Google Colab, Visual Studio Code, or RStudio Quarto, and
• Familiarity with Git commands and GitHub interface.

Class Schedule:

CRN 21877: Mondays between 14:30-17:30 at OBL1 (Computer Lab).