Feedback Control from Pixels
Explore feedback control using cameras, bridging control theory and machine learning. Learn about challenges, recent advances, and potential applications in robotics and AI.
Machine Learning Course
Comprehensive exploration of machine learning fundamentals, covering linear models, neural networks, SVMs, and key principles for effective algorithm implementation and evaluation.
Linear Regression and Linear Models
Learn powerful statistical tools: linear regression and models. Predict continuous values using various data types. Covers least squares, multiple regression, t-tests, ANOVA, and R implementation.
Comparison and Transfer Between Tasks in Overparameterized Learning
Explore similarities and differences in classification and regression tasks for overparameterized linear models. Gain insights into task transfer and generalization in high-dimensional regimes.
CBW's Introduction to R Workshop - June 21-22, 2021
Learn R programming essentials: scripting, statistical tests, loops, functions, linear models, and R Markdown in this comprehensive workshop for beginners.
Differential Equations
Explore differential equations through various techniques, including separation of variables, integrating factors, exact equations, series solutions, Laplace transforms, and systems of linear equations.
First Order Differential Equations
Learn to solve various types of first-order differential equations, including linear, exact, and Bernoulli equations, with applications and practical problem-solving techniques.
Learn Statistics with Python
Master Python for statistical analysis: design matrices, linear models, F distribution, and OLS. Hands-on tutorials using Orange and Patsy libraries for practical data science skills.
PyTorch Zero to All
Learn essential machine learning and deep learning concepts using PyTorch, covering linear models, neural networks, CNNs, and RNNs in a comprehensive series of lectures.
PyTorch for Python 3.5
Learn PyTorch fundamentals, from linear models to advanced CNNs. Master gradient descent, back-propagation, regression, and neural network architectures for efficient deep learning implementation.
Lower and Upper Bounds on the VC-Dimension of Tensor Network Models
Explorez les limites de la dimension VC des modèles de réseaux de tenseurs. Apprenez les bornes supérieures et inférieures pour divers modèles de décomposition tensorielle et leurs implications pour l'apprentissage automatique.
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.
Use Tidymodels Workflowsets to Predict Human-Computer Interactions from Star Trek
Explore machine learning with tidymodels workflowsets to predict speaker type in Star Trek interactions, featuring data preprocessing, model evaluation, and result analysis.
Predict Housing Prices in Austin TX with Tidymodels and XGBoost
Explore feature engineering for boosted tree models using Austin housing data. Learn to incorporate text information as indicator variables and tune models for accurate price predictions.
Gauss-Markov Theorem
Explore the fundamental principles and applications of the Gauss-Markov theorem in statistical analysis and linear regression modeling.
Some Geometric Properties of Spacetime - Lecture 3
Explore geometric properties of spacetime, including minimal hypersurfaces, stability, and boundary integrals. Gain insights into linear and nonlinear models in this advanced mathematics lecture.
Running Basic Statistical Analysis in R
Learn to perform essential statistical analyses in R, including correlation, linear modeling, t-tests, and heatmaps. Ideal for beginners seeking to leverage R's powerful command-line interface for data analysis.
Linear Models and Their Geometry in Machine Learning - Lecture 6
Explore the geometry and fundamental concepts of linear classifiers, examining their role as essential tools in machine learning classification problems.
Linear Classifier Expressiveness in Machine Learning - Lecture 7
Delve into the theoretical foundations of linear classifiers, exploring their expressive capabilities and understanding their role as a fundamental hypothesis class in machine learning.
Reconsidering Overfitting in the Age of Overparameterized Models
Explore modern machine learning paradigms, focusing on overparameterized models' ability to generalize well despite near-zero training error, challenging classical overfitting concepts.
What Is the Statistical Complexity of Reinforcement Learning?
Explore statistical complexity in reinforcement learning, covering supervised learning, linear models, evaluation challenges, and realizability conditions.