Random Forests
Learn to build, evaluate, and implement Random Forests, including handling missing data and clustering. Gain practical skills using R for this powerful machine learning technique.
Pattern Recognition
This course provides a fairly comprehensive view of the fundamentals of pattern classification and regression. Topics covered in the lectures include an overview of pattern classification and regression; Bayesian decision making and Bayes classifier
Balance, Criticality, and Antifragility in Complex Systems
Explore complex systems thinking and its philosophical implications with Carlos Gershenson. Delve into balance, criticality, antifragility, and the intersections between Eastern and Western philosophical traditions and modern scientific concepts.
How to Choose Model
Learn strategies for selecting the right machine learning model, including comparing models, handling noise, choosing loss functions, data splitting, and understanding assumptions.
Meta-Learning - Why It’s Hard and What We Can Do - Ke Li
Explore meta-learning challenges and solutions with Ke Li, covering optimization, model selection, and program induction in this theoretical machine learning seminar.
Regularization via Permutation of Labels - Lecture 1
Explore regularization techniques using label permutation in mathematical statistics, focusing on innovative approaches and their applications.
Quantifying CAR T Cells Activity Against AML - Model Selection Based on In Vitro Cytolytic Assays
Explore quantitative modeling of CAR T cell activity against AML through in vitro cytolytic assays, enhancing understanding of immunotherapy effectiveness.
ML in Practice - Interaction Between ML Modeling and Downstream Design Decisions
Explore ML from an engineering perspective, focusing on real-world applications and the impact of model selection on final solutions in telecommunications networks.
Data Science Solutions with Cortana Analytics
Comprehensive tutorial on building predictive models using Microsoft Azure Machine Learning, covering data processing, model creation, deployment, and practical applications in various domains.
Insights from Predictive Policing - Tobias Brandt, Rotterdam School of Management
Explore data-driven decision-support in policing, examining the complex relationship between prediction quality and decision outcomes. Learn about end-to-end frameworks for model selection in smart city operations.
AutoML Toolkit Deep Dive - Automating Feature Engineering and Model Optimization
Explore Databricks Labs' AutoML Toolkit for automating feature engineering, model selection, tuning, and deployment. Learn to accelerate machine learning workflows and enhance productivity.
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
Explore how Databricks Labs AutoML Toolkit simplifies and optimizes machine learning processes, from data preparation to model optimization, using financial loan risk data examples.
Statistical Learning III - Advanced Techniques in Machine Learning
Explore advanced statistical learning concepts with Robert Schapire from Microsoft Research, focusing on cutting-edge techniques and applications in machine learning and data analysis.
Graphon Cross-Validation: Assessing Models on Network Data
Explore graphon cross-validation techniques for network data analysis, enhancing model accuracy and generalizability through innovative validation methods and theoretical insights.
Understanding Predictive Modeling with R
Learn to create predictive models using R, covering key concepts, training techniques, and practical demonstrations. Gain insights into model selection and explore various applications in data analysis.
Fitting High-dimensional Data and Model Selection - Lecture
Explore techniques for fitting high-dimensional data and selecting appropriate models in biological evolution, focusing on polygenic adaptation and phenotypic variation.
Multiple Linear Regression: Model Building and Selection
Build and select multiple linear regression models through data loading, visualization, and iterative model refinement using R.
Statistical Inference of Logistic Regression: Confusion Matrix, Errors, and Model Selection
Explore statistical inference in logistic regression, covering confusion matrices, error types, and model selection techniques for improved predictive accuracy.
Hands-on with R: Regression Modeling and Feature Selection - Part 5
Fit regression models to temperature data, apply stepwise variable selection, and choose the best model using out-sample RMSE. Enhance skills in feature engineering and dimension reduction.
Statistical Models: Linking Data to Theory - Class 10
Explore statistical models linking data to theory in quantitative ecology. Learn from expert Diogo Melo about advanced techniques for analyzing ecological data and developing robust theoretical frameworks.
Statistical Models: Linking Data to Theory - Class 8
Explore statistical models linking data to theory in quantitative ecology. Learn advanced techniques for analyzing ecological data and developing robust theoretical frameworks.