Lectures related to Nonlinear Regression: Forecasting Trends

Introduction to Machine Learning: Regression Fundamentals

Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.

Nonparametric Regression: Kernel-Based Estimation

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Linear and Weighted Regression: Optimal Parameters and Local Solutions

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Real Estate Data Analysis: Descriptive Statistics

Explores real estate data analysis through descriptive statistics, correlation, regression, and forecasting methods.

Regression: Interactive Lecture

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

Data-Driven Modeling: Regression

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Regression Analysis: Disentangling Data

Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.

Supervised Learning: Classification and Regression

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Polynomial Regression: Overview

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Error Decomposition and Regression Methods

Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Linear Systems: Modeling and Identification

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Regression & Systemed Lineaires

Covers the principles of regression and linear systems, focusing on iterative methods.

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Regression: Linear Models

Explores linear regression, least squares, residuals, and confidence intervals in regression models.