Learning-Theoretic Foundations of Algorithm Design

Overview

Delve into the learning-theoretic foundations of algorithm design with Maria-Florina Balcan, as she shares her expertise from Carnegie Mellon University. This engaging talk reveals the integration of artificial intelligence in solving complex problems within mathematics and theoretical computer science.

Available through YouTube, this is a must-watch for anyone interested in the cutting-edge applications of AI in these fields.

Syllabus

• Introduction to Learning Theory
Overview of Machine Learning Concepts
The Role of Learning Theory in AI
• Fundamental Models of Learning
Probably Approximately Correct (PAC) Learning
Online Learning
Statistical Learning Frameworks
• Algorithm Design and Analysis
Basics of Efficient Algorithm Design
Approximation Algorithms
Randomized Algorithms
• Connections Between Learning Theory and Algorithm Design
Leveraging Learning for Algorithm Design
Learning Algorithms in Theoretical Computer Science
• Theoretical Insights into AI Applications
Applications in Mathematics
Applications in Theoretical Computer Science
• Case Studies and Real-World Applications
Case Studies from Carnegie Mellon Research
Breakthroughs in AI with Theoretical Underpinnings
• Advanced Topics in Learning-Theoretic Techniques
Game-Theoretic Learning
Multi-Armed Bandits and Exploration vs. Exploitation
• Future Directions and Open Problems
Challenges in Learning-Theoretic Algorithm Design
Emerging Research Directions in AI
• Conclusion and Summary
Recap of Key Concepts
Final Thoughts on Learning-Theoretic Foundations
• Supplemental Readings and Resources
Recommended Texts and Papers
Online Resources and Lectures
• Evaluation and Assessment
Problem Sets
Projects and Presentations
Final Examination

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Subjects

Computer Science