Machine Learning Fundamentals - Lecture 16
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
Understanding Human Trust in AI Systems - Evaluation and Analysis
Explore the complex dynamics of human-AI trust relationships, from interpersonal trust foundations to evaluating AI trustworthiness and warranted trust in practical applications.
Machine Learning Fundamentals - Lecture 15
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
Machine Learning Fundamentals - Lecture 14
Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.
Application-Grounded Evaluations of Explanations in AI Systems
Explore evaluation methods for AI explanations, focusing on simulatability, reliance metrics, and team performance in practical applications for enhanced decision-making.
Machine Learning Fundamentals - Lecture 13
Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.
Machine Learning Fundamentals - Lecture 12
Dive into advanced machine learning concepts and data science fundamentals through comprehensive theoretical discussions and practical applications in this graduate-level lecture.
Machine Learning Fundamentals - Lecture 11
Dive into advanced machine learning concepts and data science fundamentals through comprehensive theoretical discussions and practical applications at a graduate level.
Pairwise Feature Importance and Effective Attention in Self-Attention Networks
Dive into advanced concepts of attention mechanisms, exploring effective attention computation, norm-based analysis, and practical applications in neural networks.
Evaluating Input Attribution Methods in Machine Learning - Plausibility and Comprehensiveness
Explore advanced concepts in gradient-based input attribution, from plausibility assessment to comprehensive evaluation methods like ROAR and Recursive-ROAR techniques.
Machine Learning: Neural Networks and Deep Learning - Lecture 22
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level academic lecture.
Machine Learning: Neural Networks and Deep Learning - Lecture 21
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level academic lecture.
Machine Learning - Lecture 20
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
Machine Learning - Lecture 19
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
Introduction to Machine Learning Model Explanations and Interpretability
Explore key concepts in data science explanations, from gradient-based highlighting to contrastive editing, covering essential techniques for understanding and interpreting complex models.
Machine Learning Practical Concerns and Societal Impacts - Lecture 28
Explore practical neural network implementation challenges and gain valuable insights into building ethical, socially responsible machine learning systems.
Neural Networks and Backpropagation Algorithm - Lecture 27
Dive into the backpropagation algorithm and advanced neural network concepts, mastering fundamental techniques for training deep learning models effectively.
Uncertainty, Prompting, and Chain-of-Thoughts in Large Language Models - Part 2
Dive into advanced AI concepts including temperature scaling, Bayesian calibration, prompt-based finetuning, and chain-of-thought techniques for more reliable and explainable language models.
Pretrain-Finetune and Uncertainty Estimation in Machine Learning - Part 1
Dive into advanced machine learning concepts, from masked language modeling and pretrained vision models to uncertainty estimation and probability calibration techniques.
Data Preparation and Transformer Models - Introduction to Text and Image Processing
Master data preparation techniques for transformers, including text and image processing, and understand the fundamentals of transformer architecture and pretraining methods.
Introduction to Neural Networks - Lecture 25
Explore neural networks' fundamental concepts, structure, and prediction mechanisms while learning essential training approaches for building effective machine learning models.