Section | 1 |
---|---|
Instructor(s) | Singh Sahota, Davender (dsahota) |
Location | RY 277 |
Meeting Times | Wednesday 5:30pm - 8:30pm |
Fulfills | Elective Specialization - High Performance Computing (HPC-2) |
This course provides a self-contained introduction to computational data analysis from an applied perspective. It is intended as a standalone course for students who are not pursuing the full data analysis sequence in the MPCS. As such, students who have taken MPCS 53110 Foundations of Computational Data Analysis and received a grade of B or higher should take MPCS 53111 Machine Learning. Students that have taken MPCS 53111 Machine Learning cannot register for this class.
The course will cover topics in basic probability theory, statistical inference, and basic machine learning models typically used in data analysis. Each topic will be accompanied by example illustrations using computational packages and software. Many of the topics covered form the basis of almost all algorithms and machine learning methods used in big data analysis. Emphasis will be given on using these techniques for problem solving.
Textbook: An Introduction to Statistical Learning
Tentative List of Topics:
Elementary Probability and Statistics
Software Platforms
Statistical Inference and Learning
Linear Models
Model Assessment and Selection
Machine Learning Models
Clustering
Recommender Systems
Introduction to Deep Learning
MPCS 50103 Discrete Mathematics and Core Programming
Knowledge of Python is required for this class. This course requires competency in Unix and Linux. If you attended the MPCS Unix Bootcamp you covered the required material. If you did not, please review the UChicago CS Student Resource Guide here: https://uchicago-cs.github.io/student-resource-guide/.
This class is scheduled at a time that conflicts with these other classes: