Qualitative Response Model: Marginal Effects and Logit Models - Part 5
Explore qualitative response models, including marginal effects and logistic regression, to analyze categorical dependent variables effectively.
Hands-on Python: Analysis and Prediction of Bangalore House Price Data
Analyze and predict Bangalore house prices using Python, gaining practical data analysis skills for real estate market insights.
Why Julia is the Future for Data Science Projects
Explore Julia's advantages for data science, including speed, economic viability, and reduced carbon footprint compared to R and Python.
Mixing Properties of (Non-)Stationary INGARCH(1,1) Processes
Explore mixing properties of (non-)stationary INGARCH(1,1) processes in this mathematical conference talk, focusing on adaptive and high-dimensional spatio-temporal forecasting methods.
Probabilistic Machine Learning in Bioinformatics
Explore probabilistic machine learning applications in bioinformatics, focusing on cutting-edge techniques and their impact on biological data analysis.
What's New with Turing.jl and Its Ecosystem
Explore updates to Turing.jl, a probabilistic programming framework in Julia for Bayesian inference, and its expanding ecosystem of modular packages for enhanced usability and extensibility.
Using Julia as Part of an Anomaly Detection Pipeline
Explore Julia's role in anomaly detection pipelines, integrating data analysis, algorithm development, and real-time processing with Java-based web applications for efficient business solutions.
Master Data Analytics with ChatGPT, Python and Excel
Learn data analytics essentials using ChatGPT, Python, and Excel. Covers data handling, regression modeling, summary statistics, and univariate analysis with practical examples on a car MPG dataset.
Foundations of Type-Driven Probabilistic Modelling
Explore type-driven abstractions for probabilistic modelling, covering discrete probability and quasi-Borel spaces. Gain hands-on experience through accompanying exercises for self-study.
Probabilistic Programming Tutorial - Part 2
Explore advanced concepts in probabilistic programming, focusing on practical applications and techniques for scientific computing and data analysis.
Generalized Linear Models and Linear Regression - Lecture 11
Dive into advanced statistical concepts including generalized linear models, nonlinear basis functions, and regularization techniques for robust data analysis and point estimation.
Data Mining: Introduction to Linear Regression - Spring 2023
Master linear regression fundamentals, from basic concepts to least squares method, while learning how to apply these techniques for effective data analysis and predictive modeling.
Nonlinear Regression and Regularization in Data Mining - Spring 2023
Master nonlinear regression concepts, from polynomial mappings to regularization techniques, while learning practical implementation in Python and understanding cross-validation for model optimization.
Probabilistic Programming Tutorial - Part 1
Explore probabilistic programming fundamentals and applications with MIT expert Vikash Mansinghka in this comprehensive tutorial.
Predicting Healthcare Insurance Costs with Python - Real-World Data Science Problem Solving
Predict healthcare insurance costs using Python and machine learning. Clean data, visualize with pandas, and apply scikit-learn for linear regression in this hands-on tutorial with real-world data analysis.
Neural Scaling Laws - Fitting Scaling Laws for Scientific Foundation Models - Tutorial 3
Explore neural scaling laws and learn techniques for fitting scaling laws to scientific foundation models in this advanced tutorial.
Probabilistic Modeling and Programming Tutorial - Lecture 1
Explore probabilistic modeling and programming techniques with expert guidance, enhancing your AI and machine learning skills through hands-on tutorials and practical examples.
Introduction to Probabilistic Models - Probabilistic AI School
Explore probabilistic models, their applications, and fundamental concepts in this comprehensive introduction by Silja Renooij at the Nordic Probabilistic AI School.
Pseudo-Labeling for Covariate Shift Adaptation in Machine Learning
Explore advanced covariate shift adaptation techniques using pseudo-labeling methods for regression tasks, focusing on kernel ridge regression and bias-variance optimization for target distributions.
Exploiting Compositional Structure in Deep Learning - Theory and Applications
Delve into high-dimensional function modeling and deep learning applications for heart digital twins, exploring theoretical frameworks and practical implementations in medical risk prediction.
Taming Regression Using APL - From Basic Linear Models to Advanced Applications
Master statistical regression techniques using APL through practical examples, from basic linear models to advanced non-linear and logistic regression, with hands-on demonstrations using TamStat software.