Generative AI for Engineering Students/ Working Professionals

Mastering Generative AI can revolutionize your engineering career, opening doors to cutting-edge roles in AI-driven design, development, and innovation. With this expertise, you'll be in high demand as an AI Engineer, AI Researcher, or AI Consultant, and be competitive for senior roles like AI Technical Lead, AI Innovation Manager, or Chief AI Officer. You'll be empowered to create intelligent systems, automate complex tasks, and drive digital transformation in various industries. Pursue certifications like Certified AI Engineer or Certified Data Scientist to further accelerate your career. Stay ahead of the curve and unlock new opportunities in the rapidly evolving field of Generative AI.

Week 1: Introduction to Generative AI

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