Statistics 110 - Probability
Comprehensive introduction to probability theory, covering fundamental concepts, distributions, and applications. Ideal for understanding statistics, science, and randomness in various fields.
STAT115
Comprehensive lecture series on statistical methods for analyzing genomic data, covering microarrays, RNA-seq, epigenetics, cancer genomics, and more. Focuses on practical applications in bioinformatics and computational biology.
Finding Fairness - Incorporating Societal Values in Computer Algorithms
Exploring algorithmic fairness and privacy in computer science, addressing concerns about incorporating societal values into technology as it increasingly impacts our lives.
Assignment Control Plots for Causal Inference Study Design - Rocky Aikens
Explore visual tools for causal inference study design, focusing on assignment-control plots to analyze covariate distribution and improve observational and experimental studies.
Using Language Models to Understand Wage Premia
Explore how NLP techniques predict salaries from job postings, revealing wage premia for various characteristics in the digital economy labor market.
How to Be a Statistical Detective
Learn to detect statistical errors in medical literature using common sense, arithmetic, and online tools. Improve research quality and address the reproducibility crisis in science.
Stanford Seminar - Algorithmic Extremism- Examining YouTube's Rabbit Hole of Radicalization
Examination of YouTube's recommendation algorithm, challenging claims of far-right radicalization. Data analysis reveals a preference for mainstream media over fringe content, with implications for online political discourse.
Stanford Seminar - How Can We Understand and Evaluate Election Forecasts, Given That N=15 or Less
Explore election forecasting challenges, including poll analysis, nonresponse bias, and political polarization. Learn to interpret forecasts and understand limitations in predicting outcomes with limited data.
Towards Generalizable Autonomy - Duality of Discovery & Bias
Exploring generalization in embodied AI, focusing on interactive learning, representational biases, and causal inference for robotics and decision-making in complex environments.
Combining Physical and Statistical Models in Projected Global Warming
Combining physical and statistical models to narrow uncertainty in global warming projections, focusing on Earth's energy budget and model-observation relationships for more accurate future warming estimates.
Project Risk Analysis Live Information Session
Explore project risk analysis using interactive simulations and stochastic libraries. Learn cutting-edge methods applicable across industries, balancing theory with practical tools for effective risk assessment and management.
Interactive Autonomy - A Human-Centered Approach for Safe Interactions
Explore interactive autonomy for safe human-robot collaboration, focusing on algorithms that influence human behavior and efficient active learning methods to model human preferences.
Extending the Theory of ML for Human-Centric Applications
Exploring ML challenges in real-world applications, focusing on data manipulation, multi-objective optimization, and robustness in dynamic environments with human-centric considerations.
Deep Generative Models - Discrete Latent Variable Models - Lecture 17
Explore discrete latent variable models in deep generative modeling, covering key concepts and applications in AI and machine learning.
Deep Generative Models - Lecture 5: Variational Autoencoders
Explore Variational Autoencoders (VAEs) in deep generative modeling, covering theoretical foundations and practical applications in this comprehensive Stanford lecture.
Deep Generative Models - Maximum Likelihood Learning - Lecture 4
Explore maximum likelihood learning in deep generative models with Stanford's Stefano Ermon. Gain insights into advanced AI techniques and their applications in this comprehensive lecture.
Deep Generative Models - Lecture 2: Background
Explore fundamental concepts and background for deep generative models, covering essential topics to build a strong foundation in this advanced machine learning field.
Deep Generative Models - Normalizing Flows - Lecture 9
Explore normalizing flows in deep generative models, focusing on their principles, applications, and recent advancements in this Stanford lecture by Prof. Stefano Ermon.
Deep Generative Models - Energy Based Models - Lecture 14
Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights from Stanford's expert on advanced generative modeling techniques.
Deep Generative Models - Energy Based Models - Lecture 11
Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights from Stanford's expert on advanced generative modeling techniques.
Stanford CS236: Deep Generative Models - Lecture 7 - Normalizing Flows
Explore normalizing flows in deep generative models with Stanford's Stefano Ermon. Learn advanced techniques for creating flexible and expressive probability distributions in machine learning.