The More Data, the Better the AI, Isn't It?

Overview

Join us for an enlightening session on data quality in the realm of AI and deep learning. Discover the crucial role of maintaining high-quality datasets to enhance algorithm performance, especially in the context of document data extraction.

This talk, featuring expert insights, is a must for anyone interested in the intersection of data quality and artificial intelligence.

Hosted by the University and available on YouTube, this event falls under two key categories:

AI Courses and Conference Talks. Perfect for AI practitioners, data scientists, and those keen on understanding the intricacies of data quality in AI systems.

Don’t miss out on this opportunity to enhance your knowledge and skills in managing datasets for successful AI implementations.

Syllabus

• Introduction to Data Quality in AI
Importance of Data Quality
Overview of AI and Deep Learning
• Understanding Data in AI Models
Types of Data: Structured vs. Unstructured
Introduction to Document Data Extraction
• Data Quality Dimensions
Accuracy and Completeness
Consistency and Timeliness
Relevance and Validity
• Data Collection and Preparation
Sources of Data for AI
Strategies for Data Cleaning
Handling Missing and Noisy Data
• Challenges in Document Data Extraction
Optical Character Recognition (OCR) Issues
Data Annotation and Labeling Challenges
Handling Complex and Unstructured Documents
• Tools and Techniques for Ensuring Data Quality
Data Quality Assessment Frameworks
Automation in Data Cleaning
Use of AI to Improve Data Quality
• Maintaining High-Quality Datasets
Continuous Monitoring and Validation
Importance of Feedback Loops
Data Governance and Best Practices
• Impact of Data Quality on AI Performance
Real-World Case Studies
How Data Quality Affects Model Accuracy and Bias
• Future Trends in Data Quality for AI
Emerging Technologies in Data Quality Management
The Role of Synthetic Data
• Wrap-Up and Course Conclusion
Key Takeaways
Discussion on Industry Applications and Ethics
Final Q&A Session
• Additional Resources and Further Reading

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Subjects

Conference Talks