Teaching

2023

CS5804: Introduction to Artificial Intelligence. Spring 2023 Course Syllabus

_{Student Project Showcases}

• Team1: Evalutaion of AI Agents in Ultimate Tic-Tac-Toe
• Team2: New Approach on 2048
• Team3: DeepStack Poker
• Team4: Enhanced Battle City
• Team5: Image Upscaling by code prediction (CodeFormer)
• Team6: Applying Transformer-Based NLP Techniques on Education Tweets
• Team7: Fetching the Right Breed
• Team8: Deep Learning Based High Accuracy Traffic Sign Detection
• Team9: Snake Game Automation and Optimization
• Team10: Vision-based Human Activity Recognition (HAR) using Transfer Learning Approach for Internet-of-Things (IoT) Applications
• Team11: Yelp Review Rating Prediction
• Team12: Artificial Intelligence Models for Image Classification
• Team13: Data Augmentation in Medical Image Datasets with DCGAN
• Team14: Traffic Prediction with ML and DL Algorithms
• Team15: Chess AI Agent
• Team16: PREDICTING MLB GAMES USING A MULTILAYER PERCEPTRON NEURAL NETWORK
• Team17: Hacking Natural Selection
• Team18: Visual Query Response
• Team19: Neural Networks for Sentence Classification
• Team20: A Multi-fidelity Prediction with Convolutional Neural Networks Using High-Dimensional Data

Undergrade Independent Study

• _{Sid Pothineni}

2022

CS4804: Introduction to Artificial Intelligence. Fall 2022 Course Syllabus

CS5804: Introduction to Artificial Intelligence. Spring 2022 Course Syllabus

_{Student Project Showcases}

• Team1: Developing a Chess AI
• Team2: Colorizing Grayscale Images with Convolutional Neural Networks (CNN)
• Team3: Comparing ML Techniques for Authenticating Banknotes
• Team4: Applying Deep Q-Learning to Atari Games
• Team5: Sign Pose-Based TRANSFORMER For Word-Level Sign Language Recognition [SPOTER]
• Team6: AI based Ludo Player
• Team7: Instance Segmentation of Edamame Pods using Deep Learning
• Team8: Analyzing and Modeling for Predicting Stroke
• Team9: Using Vision Transformers to Classify Bird Species
• Team10: Predicting who wins the DOTA 2 game
• Team11: Applying Deep Q-learning approach to Pacman problem
• Team12: Sentiment Analysis
• Team13: Pixel to Code Introduction
• Team14: StarGAN: the Advanced Appearance Simulator
• Team15: A comparative study of temporal difference methods on self-driving cars
• Team16: Image Classification & Evaluation by Machine Learning
• Team18: Credit Card Fraud Detection
• Team19: Imperfect-Information Problems
• Team20: Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation

Honors College Coursework

• _{Lennon Headlee}

2021

CS4804: Introduction to Artificial Intelligence. Fall 2021 Course Syllabus

CS4604: Introduction to Database Management Systems. Spring 2021 Course Syllabus

Undergrade Research

• _{Paranav Sharma}

2020

CS4804: Introduction to Artificial Intelligence. Fall 2020 Course Syllabus

2017-2018

_{Amazon Web Services (AWS) essentials and architecting for Data Analytics (Part 1)}

        _{This course provides an introduction to AWS and offers a broad overview of terminology and concepts. The course will focus on the AWS web console, CLI tools, and data storage needed to be successful in data management on AWS. This course is intended as a prerequisite for the companion NLI course, Data Analytics using Amazon Web Services (AWS).
        This course will first give a broad overview of AWS terminology while giving a tour of the AWS web console. We will talk about users and user permissions and introduce the concept of least privilege. We will introduce the AWS CLI tools, and give an overview of what they can be used for. Then we will learn how to use these tools to do basic tasks like storing data and giving a user access to that data.}

_{Data Analytics using Amazon Web Services (AWS) (Part 2)}

        _{This course builds on the earlier course "Amazon Web Services‎ (AWS) essentials and architecting for Data Analytics." This course provides an overview of Amazon Web Services (AWS) services that can help you perform data analytics for your research in the AWS ecosystem. The course instructor who has received AWS Instructor-led training and AWS certifications will teach you how to use advanced AWS services, such as Amazon EMR, Amazon Redshift, and Amazon Kinesis. The course includes a combination of lectures and hands-on labs that expose students to these techniques.
        You will learn about creating big data environments, choosing appropriate AWS data storage options, working with Amazon RDS, Amazon DynamoDB, Amazon Redshift, and Amazon Kinesis, etc. and leveraging best practices to architect workflows to process research dataset and retrieve results in a cost-effectiveness way. This course is intended for faculty and students who are interested in learning about the advanced AWS services for performing data analytics in the cloud environment.}

Workshop

2020

_{Building a full-stack Serverless Web application with React and AWS}

        _{Serverless computing allows you to build Web applications without managing or maintaining servers. Using AWS, we can build and deploy responsive applications in the cloud with built-in high availability and flexible scaling capabilities. In this workshop, we will learn how to build a full-stack serverless Web application using React and several AWS services, including AWS Amplify, Lambda, AppSync, DynamoDB, etc. We’ll start the workshop with a quick overview of serverless computing and AWS, followed by creating a React application, integrating with AWS managed services and deploying this application in AWS. Conference URL}

2019

_{Building a full-stack serverless Web application with AWS Amplify}

        _{In this workshop, we'll learn how to build a full-stack serverless Web application AWS Amplify. The techniques we use in this workshop including React, AWS Amplify, AWS AppSync,DynamoDB, and other AWS services.}

2018

_{Code4lib 2018: Interacting with Standards, Hands-on Fedora}

        _{This workshop will focus on understanding and experiencing the interaction models defined by the web-standards that are contained in the Fedora Repository API. Specifically:}

• _Memento
• _{Web Access Control}
• _{Linked Data Platform}
• _{Activity Streams}

Teaching certifications

• _{Virginia Tech - Future Professoriate Certificate}
• _{Virginia Tech - TLOS: Professional Development Network (PDN) Instructors}
• _{Virginia Tech - TLOS: Professional Development Network (PDN) Advanced Instructor}
• _{Virginia Tech - TLOS: Professional Development Network (PDN) Master Instructor}