Reliable Data Driven Decision Making Systems

Overview

Explore the fundamentals of building reliable AI decision-making systems, focusing on data-driven approaches and system reliability principles presented by Berkeley AI Research experts.

Syllabus

• Introduction to Data-Driven Decision Making Systems
Overview of AI Decision-Making
Importance of Reliability in AI Systems
• Understanding Data in AI Systems
Data Collection Techniques
Data Preprocessing and Cleaning
Data Quality and its Impact on AI Decisions
• System Reliability Principles
Definitions and Metrics of System Reliability
Design Principles for Reliable AI Systems
Maintaining Reliability in Dynamic Environments
• Algorithms for Reliable Decision-Making
Review of Machine Learning Algorithms
Techniques for Enhancing Algorithm Robustness
Selecting the Right Algorithms for Reliability
• Evaluation and Testing of AI Systems
Methods for Testing AI System Reliability
Use of Simulations and Real-World Testing
Metrics for Evaluating System Performance
• Bias and Fairness in AI-Driven Systems
Identifying and Mitigating Bias in Data
Techniques for Ensuring Fair Decision-Making
Ethical Implications of AI Decisions
• Case Studies and Applications
Case Study Analysis of Reliable AI Systems
Lessons Learned from Industry Applications
Emerging Trends and Future Directions
• Capstone Project: Building a Reliable AI System
Project Design and Planning
Implementation Using Real-World Data
Presentation and Peer Review of Projects
• Conclusion and Future Outlook
Review of Key Concepts
Discussion on the Future of Reliable AI Systems
Q&A and Closing Remarks

Subjects

Business