Prerequisite: CMP SCI 4342, or CMP SCI 2261 and CMP SCI 3130, or consent of instructor.

This course provides an introduction to machine learning principles, algorithms and applications. Topics may include computational learning theory, VC Dimension, generalization, classification, regression, regularization, validation, and reinforcement learning. Credit cannot be granted for both CMP SCI 4340 and CMP SCI 5340.

Prerequisite: Graduate standing in Computer Science/Cybersecurity (Computer Science option) or CMP SCI 4342 or CMP SCI 5342 or consent of the instructor.

This course provides an introduction to machine learning principles, algorithms and applications. Topics may include computational learning theory, VC dimensions, generalization, classification, regression, regularization, validation, and reinforcement learning. Credit cannot be granted for both CMP SCI 4340 and CMP SCI 5340.

Prerequisite:  CMP SCI 1250, MATH 1900, and an introductory statistics course (ANTHRO 3220/SOC 3220, BIOL 4122, CRIMIN 2220, ECON 3100, MATH 1320, POL SCI 3000, or PSYCH 2201)

This course covers statistical inference with emphasis on applications and computer simulation. Topics may include multivariate distributions, transformations and combinations of random variables, sampling distributions, maximum likelihood, bootstrap, order statistics, hypothesis testing, likelihood ratio tests, Monte Carlo methods, Bayesian inference, and sufficient statistics. Students may not receive credit for both CMP SCI 4151 and CMP SCI 5151.

Prerequisite:  Graduate standing in Computer Science/Cybersecurity (Computer Science option) or, for non-Computer Science graduate students, consent of the instructor.

This course covers statistical inference with emphasis on applications and computer simulation. Topics may include multivariate distributions, transformations and combinations of random variables, sampling distributions, maximum likelihood, bootstrap, order statistics, hypothesis testing, likelihood ratio tests, Monte Carlo methods, Bayesian inference, and sufficient statistics. Students may not receive credit for both CMP SCI 4151 and CMP SCI 5151.

Prerequisite:  Graduate standing in Computer Science

This course covers the design of efficient data structures and algorithms, as well as an advanced analysis of the time and space complexities of iterative and recursive algorithms. Students will learn a variety of techniques including dynamic programming, greedy algorithms, various graph algorithms, and NP-completeness and approximation algorithms.

Prerequisite:  Graduate standing in Computer Science

This course covers the design of efficient data structures and algorithms, as well as an advanced analysis of the time and space complexities of iterative and recursive algorithms. Students will learn a variety of techniques including dynamic programming, greedy algorithms, various graph algorithms, and NP-completeness and approximation algorithms.

Prerequisite:  CMP SCI 2250, MATH 1320, and MATH 3000.

This course addresses the design and analysis of fundamental algorithms in computer science. Topics include basic sorting algorithms, priority queues, order statistics, search trees, and hash tables. Analysis techniques may involve time and space complexity analysis of both iterative and recursive algorithms and analysis of algorithm correctness.

Role: Advisor

Data collection, integration, and curation for the crowd-sourced annotation purpose.

Role: Instructor / Project Advisor

Prerequisite:  Fundamentals of Data Science (DSCI 4780) with a grade of C or higher (Students must meet the Data Science major eligibility requirement to be able to register for the class.)

The purpose of the Capstone Project is for students to apply theoretical knowledge acquired during the Data Science program to a project involving actual data in a realistic setting. During the project, students engage in the entire process of solving a real-world data science project, from collecting and processing actual data to applying suitable and appropriate analytic methods to the problem. Serves as a Critical Thinking Through Writing course.

Role: Instructor / Project Advisor

Prerequisite: Machine Learning (CSC 6850 / CSC 8850) and/or Deep Learning (CSC 8851)

In continuation of the Summer Code Sprint 2020, I am currently teaching the Summer Code Sprint 2021 for the Computer Science and Data Science and Analytics programs in Georgia State University. 

During this 7-week program we will be exploring a number of feature subset selection algorithms for high-dimensional data. This will require efficient programming, familiarity with docker containers and Unix systems for connecting to DMLab's server and using our computing resources.

Role: Instructor / Project Advisor

Prerequisite: Machine Learning (CSC 6850 / CSC 8850) and/or Deep Learning (CSC 8851)

This was a course I designed as a Summer Code Sprint for the Data Science and Analytics program (concentration of Big Data & Machine Learning) in Georgia State University. This Sprint was organized by DMLab to provide some practical training in Machine Learning on Big Data while exploring some new avenues in Time Series Classification on Heliophysics Big Data.

During this 7-week program students were closely guided through different paths toward a shared objective which was classification of solar flares using Machine Learning. Students were exposed to the complexity of multi-class and high-dimensional data, issues caused by the extreme class-imbalance ratio, technical implementation of different analytical tools, and building upon their theoretical knowledge of Machine Learning and Data Mining.

Role: Lecturer

This was a hands-on class for ∼40 undergraduate and graduate students, covering the fundamentals of Data Mining such as data preprocessing, dimensionality reduction, frequent pattern mining, supervised and unsupervised learning models, and model evaluation methods. The lecture slides, assignments, and the students’ posters for their final projects are available at the course web page.