Generative AI for Engineering Students/ Working Professionals

Lecture 1: Overview of AI and Machine Learning

  - Definition and history of AI

  - Fundamentals of AI and ML. 

Lecture 2: Introduction to Generative AI

  - Definition and significance

  - Key applications in engineering

Week 2: Fundamentals of Generative Models

Lecture 3: Probabilistic Models

  - Bayesian networks

  - Markov models

Lecture 4: Neural Networks and Deep Learning

  - Basics of neural networks

  - Introduction to deep learning

Week 3: Advanced Generative Models 

Lecture 5: Transformer Models

  - Architecture and working principles

  - Applications in text generation

Lecture 6: Diffusion Models

  - Basics and applications

  - Comparison with other generative models

Week 4: Practical Applications in Engineering 

Lecture 7: Generative AI in Design and Manufacturing 

  - CAD design automation

  - Generative design in manufacturing

Lecture 8: Generative AI in Robotics and Automation 

  - Path planning and control

  - Simulation and training of robots

Week 5: Ethical and Societal Implications 

Lecture 9: Ethical Considerations

  - Bias and fairness in generative models

  - Privacy concerns

Lecture 10: Societal Impact 

  - Job displacement

  - Future trends and opportunities

Week 6: Hands-on Projects and Case Studies

Lecture 11: Project Introduction and Guidelines

  - Overview of project requirements

  - Team formation and project planning

Lecture 12: Case Studies

  - Real-world applications of generative AI

  - Success stories and lessons learned

Course Wrap-up and Future Directions

  - Recap of key concepts and learnings

  - Q&A session

Future Directions in Generative AI

  - Emerging trends and technologies

  - Career opportunities in generative AI