Free Advanced GenAI Course From Professors at Ivy League Universities

Advanced Generative Models Course explores foundational probabilistic principles, learning algorithms, and popular model families in generative models. It covers applications in computer vision, natural language processing, and biomedicine, while drawing connections to reinforcement learning.

Instructors:
- Volodymyr Kuleshov (Cornell Tech): Focuses on machine learning for scientific discovery, health, and sustainability.
- Aditya Grover (UCLA): Researches probabilistic generative modeling and sequential decision making.
- Yang Song (CalTech): Develops methods for high-dimensional data modeling.
- Stefano Ermon (Stanford): Works on probabilistic modeling for societal relevance.
- Hongjun Wu (Cornell Tech): Applies machine learning in 3D animation and video games.

What's Inside:
- Core Concepts & Techniques: Fundamentals of VAEs and GANs.
- Practical Applications & Projects: Hands-on projects in various domains.
- Integration & Enhancement: Advanced techniques for combining generative models.

General Information:
- Understanding Generative Models: Learn how models like VAEs and GANs generate data.
- Model Architecture Mastery: Study the structure and benefits of different models.
- Integration Techniques: Combine models for sophisticated outputs.
- Real-world Applications: Apply knowledge in art, gaming, and synthetic data generation.
- Ethical AI Use: Explore ethical considerations in AI deployment.

Prerequisites:
- Background in mathematics and programming (Master's level).
- Basic knowledge in machine learning, probabilities, calculus, and deep neural networks.
- Proficiency in a programming language, preferably Python.