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

Instructor(s) | Mayampurath, Anoop (anmayampurath) |

Location | Ryerson 277 |

Meeting Times | Tuesday 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 do not want to pursue the full data analysis sequence in the MPCS. As such, students who have taken or are taking MPCS 53111 Machine Learning cannot register for this class. Students who have taken MPCS 53110 Foundations of Computational Data Analysis must obtain MPCS administration approval before registering 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. All work will be done in R (https://www.r-project.org/about.html). Week 1: Elementary Probability Statistics

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

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

- Least-squares regression
- Logistic regression
- Hypothesis testing

Week 5: Machine Learning Models I

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

Week 7: Clustering

- Unsupervised clustering
- Supervised clustering

- Support vector machines

- Common machine learning frameworks
- Big data analytics

MPCS 50103 (Discrete Mathematics) and Core Programming

This class is scheduled at a time that conflicts with these other classes:

- MPCS 51050-1 -- OO Architecture: Patterns, Technologies, Implementations
- MPCS 56420-1 -- Bioinformatics for Computer Scientists
- MPCS 51400-1 -- Functional Programming