Generative AI for Engineers

GMUSD66

Credits: 2.00

Prerequisites : Basic understanding of machine learning concepts and proficiency in Python programming

Course Duration : 30 Hours / 12 Weeks

Start Date : 2024-03-28

End Date : 2024-03-30

Seats Remaining : 60

Domain: Engineers, Computer Science, and Information Technology students interested in generative AI

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Aim & Summary

The course covers fundamental concepts such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning for generative tasks. Participants will explore applications of generative AI in image generation, text generation, music composition, and beyond. Through hands-on projects and practical exercises, participants will gain experience in implementing and fine-tuning generative models using popular frameworks such as TensorFlow and PyTorch. By the end of the course, participants will have the skills to develop custom generative AI solutions tailored to engineering applications.

Course Content
Week 1	Introduction to Generative Artificial Intelligence
Week 2	Fundamentals of Generative Models: GANs, VAEs, and Autoregressive Models
Week 3	Understanding Generative Adversarial Networks (GANs)
Week 4	Variational Autoencoders (VAEs) and Latent Space Representation
QUIZ 1
Week 5	Deep Reinforcement Learning for Generative Tasks
Week 6	Image Generation with GANs: DCGANs, StyleGAN, and Conditional GANs
Week 7	Text Generation with Recurrent Neural Networks (RNNs) and Transformer Models
Week 8	Music Generation with Deep Learning: MIDI Generation and Audio Synthesis
QUIZ 2
Week 9	Applications of Generative AI in Engineering: Design Optimization, Anomaly Detection
Week 10	Transfer Learning and Fine-Tuning Pretrained Generative Models
Week 11	Ethics and Bias in Generative AI
Week 12	Project Development: Building Custom Generative AI Systems
PROJECT REPORT

Course Content

Week 1

Introduction to Generative Artificial Intelligence

Week 2

Fundamentals of Generative Models: GANs, VAEs, and Autoregressive Models

Week 3

Understanding Generative Adversarial Networks (GANs)

Week 4

Variational Autoencoders (VAEs) and Latent Space Representation

QUIZ 1

Week 5

Deep Reinforcement Learning for Generative Tasks

Week 6

Image Generation with GANs: DCGANs, StyleGAN, and Conditional GANs

Week 7

Text Generation with Recurrent Neural Networks (RNNs) and Transformer Models

Week 8

Music Generation with Deep Learning: MIDI Generation and Audio Synthesis

QUIZ 2

Week 9

Applications of Generative AI in Engineering: Design Optimization, Anomaly Detection

Week 10

Transfer Learning and Fine-Tuning Pretrained Generative Models

Week 11

Ethics and Bias in Generative AI

Week 12

Project Development: Building Custom Generative AI Systems

PROJECT REPORT

Course Certification

Certificate will have your name, photograph and the score in the final exam with the breakup. It will have the logos of GMU and company handling the course.