| Section | 1 |
|---|---|
| Instructor(s) | Singh Sahota, Davender (dsahota) |
| Location | Ryerson |
| 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 or are currently enrolled in 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 Python. Many of the topics covered form the basis of almost all algorithms and machine learning methods used in data analysis. As an applied course, the emphasis will be on the use of these tools to solve problems.
Textbook: An Introduction to Statistical Learning
Optional: The Elements of Statistical Learning; Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython; Deep Learning with Python
Course Contents:
Evaluation:
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: