Section | 1 |
---|---|

Instructor(s) | Singh Sahota, Davender (dsahota) |

Location | JCL 011 |

Meeting Times | Saturday 9am - 12pm |

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 Schedule:

Week 1: Elementary Probability Statistics

- Course overview
- Probability theory
- Random variables
- Distributions and densities

Week 2: Software Platforms

- Variables, objects, and functions in Python
- Working with data frames
- Data pre-processing and visualization

Week 3: Linear Models/Statistical Inference

- Least-squares regression
- Logistic regression
- Hypothesis testing

Week 4: Model Assessment and Selection

Week 5: Machine Learning Models I

- Perceptron classifier
- Neural networks
- Decision trees/Random forests

Week 6: Project pitches

Week 7: Clustering

- Unsupervised clustering
- Supervised clustering

Week 8: Machine Learning Models II

- Decision trees/Random forests
- Support vector machines

Week 9 : Computational Frameworks

- Common machine learning frameworks
- Big data analytics

Finals Week: Project Presentations

MPCS 50103 Discrete Mathematics and Core Programming

Knowledge of Python is required for this class. This course requires competency in Unix and Linux. Please plan to attend the MPCS Unix Bootcamp (https://masters.cs.uchicago.edu/page/mpcs-unix-bootcamp) or take the online MPCS Unix Bootcamp Course on Canvas.

This class is scheduled at a time that does not conflict with any other classes this quarter.

Masters Program in Computer Science
Bx/MS in Computer Science (Option 2: Professionally-oriented - CS Majors)
Bx/MS in Computer Science (Option 3: Profesionally-oriented - Non-CS Majors)