Quantitative Social Science Methods
First course for incoming Harvard Government Department PhD students; also taken by students in other departments, and students elsewhere through the Harvard Extension School. Instructor: Gary King.
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.
Metric Forecasting with Dynatrace - Predictive IT Resiliency Automation
Explore probabilistic forecasting for cloud resource management. Learn mathematical models, leverage forecasts in Notebooks, and automate infrastructure scaling using Dynatrace's predictive IT resiliency.
Scalable Single-Cell Models for eQTL Mapping - Primer and Main Talk
Explore scalable single-cell models for robust cell-state-dependent eQTL mapping. Learn about statistical challenges, computational solutions, and applications in identifying autoimmune disease risk loci in memory T cells.
Binomial Distribution
Learn to calculate and interpret binomial probabilities using formulas, tables, and graphs. Explore the binomial distribution as a statistical model for real-world applications.
Effective Data Storytelling as a Data Scientist in Industry
Learn effective data storytelling techniques for industry presentations. Avoid common mistakes, improve communication of ML models, and craft compelling data stories to enhance stakeholder relationships.
Successes and Challenges in Neural Models for Speech and Language - Michael Collins
Explore neural models' impact on speech and language processing, examining successes, challenges, and key architectures in parsing, word embeddings, and transformers.
Community Detection in Weighted Networks
Explore community detection in weighted networks using Gaussian models. Learn about a fast EM algorithm for optimal community label recovery in complex random systems.
What Does It Take to Be a Good Fisherman?
Explore statistical challenges in learning from biased data, focusing on self-selection issues in various fields and discussing recent progress in econometric modeling and auction theory.
Spotless Sandboxes: Evading Malware Analysis Systems Using Wear-and-Tear Artifacts
Explore techniques for evading malware analysis systems by exploiting wear-and-tear artifacts on real devices, with insights on detection accuracy and potential defensive strategies.
Negativity and Semantic Change - Will Hamilton, Stanford University
Explore dynamic word embeddings to test semantic instability in negative vs. positive language, with implications for sentiment analysis tools and linguistic evolution.
Data Science Applications - Environment/Ecology
Explore statistical modeling techniques for ecological data, focusing on hidden Markov models, Bayesian inference, and capture-recapture methods. Learn to analyze and interpret complex environmental datasets.
Statistical Inference I
Explore statistical inference fundamentals, including models, estimation, minimax risk, and Bayesian approaches, with UC Berkeley's Will Fithian.
Learning Real-World Probabilistic Models with Approximate Message Passing
Explore probabilistic modeling for big data using factor graphs and message passing. Learn how Amazon tackles real-world problems with advanced statistical techniques in the era of structured data.
Unveiling the Mysteries in Spatial Gene Expression
Explore cutting-edge statistical methods for analyzing spatial gene expression data, uncovering cellular mysteries and advancing computational biology with Professor Bin Yu.
Validate and Monitor Your AI and Machine Learning Models
Discover techniques for validating and monitoring advanced AI and ML models, ensuring long-term performance and business adoption in various industries.
Statistical Rethinking 2022 - Golem of Prague
Explore statistical modeling, scientific workflow, and causal inference through the lens of golems, owls, and DAGs in this thought-provoking introduction to statistical rethinking.
The Kikuchi Hierarchy and Tensor PCA
Explores advanced algorithms for tensor PCA, introducing a new hierarchy inspired by Kikuchi free energy to improve belief propagation performance and match sum-of-squares methods.
Estimating the Reach of a Manifold
Explore estimating manifold reach in geometric inference, covering problem formulation, statistical models, and minimax estimates for global and local cases.
New Challenges in Computational Statistics
Explore cutting-edge computational statistics challenges and innovations in this lecture by Professor Gareth Roberts, focusing on complex data analysis and algorithm advancements in statistical science.
Aggregated Tests Based on Supremal Divergence Estimators for Non-Regular Statistical Models
Explore advanced statistical techniques for non-regular models using supremal divergence estimators and aggregated tests. Enhance analytical skills for complex data scenarios.