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Computing in Python I: Fundamentals and Procedural Programming
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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
Explore the complex dynamics of human-AI trust relationships, from interpersonal trust foundations to evaluating AI trustworthiness and warranted trust in practical applications.
Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.
Explore evaluation methods for AI explanations, focusing on simulatability, reliance metrics, and team performance in practical applications for enhanced decision-making.
Dive into advanced machine learning concepts and data science fundamentals through comprehensive theoretical discussions and practical applications in this graduate-level lecture.
Dive into advanced machine learning concepts and data science fundamentals through comprehensive theoretical discussions and practical applications at a graduate level.
Delve into advanced mathematical concepts of data influence, exploring influence functions, efficiency, and convexity through theoretical frameworks and practical applications in data science.
Dive into advanced concepts of attention mechanisms, exploring effective attention computation, norm-based analysis, and practical applications in neural networks.
Explore the concept of Select-then-Predict methodology, comparing post-hoc and inherent model interpretability while examining key approaches like differentiable binary variables and Trojan explanations.
Explore advanced concepts in gradient-based input attribution, from plausibility assessment to comprehensive evaluation methods like ROAR and Recursive-ROAR techniques.
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level academic lecture.
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