Introduction to Generative AI vs. Traditional ML

Übersicht

Understanding the difference between traditional machine learning and generative AI is crucial for making informed decisions about AI implementation. In this course, Introduction to Generative AI vs.

Traditional ML, you’ll learn to evaluate, compare, and choose the best AI approach for your projects. First, you’ll explore the fundamentals of traditional ML, including supervised, unsupervised, and reinforcement learning.

Next, you’ll discover how generative AI models like transformers, GANs, and diffusion models work and where they excel. Finally, you’ll learn how to select the right approach based on data availability, business needs, and computational resources.

When you’re finished with this course, you’ll have the skills and knowledge of both traditional ML and generative AI needed to make strategic AI decisions and apply the right technology to your projects effectively.

Lehrplan

• Introduction
Course Overview and Objectives
Importance of Understanding AI Paradigms
• Fundamentals of Traditional Machine Learning
Supervised Learning
Definition and Examples
Key Algorithms: Linear Regression, Decision Trees, SVM
Unsupervised Learning
Clustering and Dimensionality Reduction
Key Algorithms: K-means, PCA
Reinforcement Learning
Basics of Rewards and Policies
Applications and Key Algorithms
• Introduction to Generative AI
Overview of Generative Models
Key Concepts in Generative AI
• Generative AI Models
Transformers
Transformer Architecture
Applications in NLP and Beyond
Generative Adversarial Networks (GANs)
How GANs Work: Generator vs. Discriminator
Popular GAN Architectures
Diffusion Models
Understanding Diffusion Processes
Recent Advances and Applications
• Comparing Generative AI with Traditional ML
Strengths and Limitations of Each Approach
Use Cases and Industry Applications
Case Studies: Comparing Outcomes and Efficiencies
• Decision-Making Framework for AI Implementation
Evaluating Data Availability and Quality
Assessing Business Needs and Goals
Consideration of Computational Resources
• Applying Theory to Practice
Hands-on Project: Selecting AI Approaches for Real-World Scenarios
Tools and Platforms for Implementation
• Course Conclusion
Recap of Key Learning Outcomes
Future Directions in AI Development and Strategy
• Additional Resources and References

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Unterrichtet von

Alper Tellioglu

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Fachgebiete

Computer Science