Teaching

1. • Week 1: Introduction to Distributed Network Systems & Internet-of-Things (notes) 

   • Week 2: IoT System Architecture and Distributed Models (notes)

   • Week 3: Communication Models in Distributed IoT Systems (notes)

   • Week 4: IoT Protocol Stacks and Web-Based Communication (notes)

   • Week 5: Security and Privacy in Distributed IoT Systems (notes)

   • Week 6: Self-Study Assignment: Time, Coordination and Reliability in IoT Systems (pdf) (submission)

   • Week 7: Transactions, Fault Tolerance, and Replication (notes)

     • • • IoT Workshops Guideline (pdf)

Week 9: Midterm exam

Week 10: IoT Workshop 1 (NodeMCU GPIO Control with LED and Push Button)

Week 11: IoT Workshop 2 (Temperature and Humidity Monitoring using DHT11)

Week 12: IoT Workshop 3 (Smart Lighting System using LDR Sensor)

Week 13: IoT Workshop 4 (Distance Measurement using Ultrasonic Sensor)

Week 14: IoT Workshop 5 (Smart Environment Monitoring System)

1. • Week 1: Introduction (notes) (Materials)

   • Week 2: Ethical Considerations (notes) (Materials)

   • Week 3: Foundations of Statistics for Data Science (notes) (Materials)

   • Week 4: Applied Statistics for Data Science Using Python (notes) (Materials)

   • Week 5: Machine Learning Fundamentals (notes) (Materials)

   • Week 6: Metaheuristic Algorithms (notes) (Materials)

   • Week 7: Neural Networks and Deep Learning (notes) (Materials)

   • Week 9: Cognitive Modeling (notes) (Materials)

   • Week 10: Social Dynamics Analysis (notes) (Materials)

     • • Final Project Description for Data Science Course (pdf)

       • Final Report and Presentation Guidelines (pdf)

       • Little help with Deep Learning code (Colab)

   • Week 11: Data Visualization (notes) (Materials)

   • Week 12: Modeling and Simulation in Computational Neuroscience (notes) (Materials)

   • Week 13: GENERATIVE AI: Exploring the Present and Future Significance of Generative AI (notes) (Materials)

   • Week 14: Data Science Trends  (notes) (Materials)

Week 1: Introduction and Select a Business Case (notes)

Week 2: Rule-Based Systems & Fuzzy Logic (notes)

Week 3: Case-Based Reasoning (CBR) for Customer Behavior & Ethics (notes)

Week 4: ABMS for Customer Behavior – Part 1 (notes)

Week 5: ABMS for Customer Behavior – Part 2 (notes)

Week 6: Deep Learning with ABMS – Part 1 (notes)

• • Little help with Deep Learning code (Colab)

Week 7: Deep Learning with ABMS – Part 2 (notes)

Week 9: midterms exam

Week 10: Building Flask-RESTful APIs for Customers – Part 1 (notes)

• • Little help with Flask-RESTful APIs code (Colab)

  • Flask RESTful API Usage Guide (pdf)

Week 11: Building Flask-RESTful APIs for Customers – Part 2 - Database Integration & Firebase Deployment (notes)

Week 12: Flask APIs for Customer Dashboards (notes)

Week 13: Flask APIs for Customer IoT (notes)

Week 14: Flask APIs for Customer Automation (notes)

Intelligent Systems Final Project Submission (URL)