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Linear Regression

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Related lectures (41)

Linear Regression Basics Log in to Mediaspace to watch this video

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

Understanding Data Attributes Log in to Mediaspace to watch this video

Covers the analysis of various data attributes and linear regression models.

Regression: Interactive Lecture Log in to Mediaspace to watch this video

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

Regression: High Dimensions Log in to Mediaspace to watch this video

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Supervised Learning Fundamentals Log in to Mediaspace to watch this video

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Linear Models: Part 1 Log in to Mediaspace to watch this video

Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.

Linear Regression: Basics and Applications Log in to Mediaspace to watch this video

Covers the basics of linear regression in machine learning, exploring its applications in predicting outcomes like birth weight and analyzing relationships between variables.

Basics of Linear Regression Log in to Mediaspace to watch this video

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Regression: Simple and Multiple Linear Log in to Mediaspace to watch this video

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Basics of linear regression model Log in to Mediaspace to watch this video

Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.

Supervised Learning in Financial Econometrics Log in to Mediaspace to watch this video

Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.

Regression Reloaded: Synthetic Dataset Construction and Parameter Estimation Log in to Mediaspace to watch this video

Covers the construction of a synthetic dataset for linear regression and the estimation of ground truth parameters.

Modern Regression: Spring Barley Data Log in to Mediaspace to watch this video

Covers iterative weighted least squares, Poisson regression, and Bayesian analysis of spring barley data using mixed models.

Recursive Least Squares Log in to Mediaspace to watch this video

Explains the Recursive Least Squares algorithm for updating parameter estimates in linear regression models.

Regression Modeling: Variable Selection Log in to Mediaspace to watch this video

Explores regression modeling, emphasizing the importance of interpretation and prediction in model building.

Principal Component Analysis: Applications and Limitations Log in to Mediaspace to watch this video

Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.

Optimal Least Square Adaptive Filter: FIR Filter Log in to Mediaspace to watch this video

Explores the Optimal Least Square Adaptive Filter, focusing on the Finite Impulse Response (FIR) Filter and strategies to reduce computational burden.

Machine Learning Fundamentals Log in to Mediaspace to watch this video

Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.

Deep Learning: Convolutional Neural Networks and Training Techniques Log in to Mediaspace to watch this video

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Detecting and Correcting Parameter Errors in Power Grids Log in to Mediaspace to watch this video

Covers the detection and correction of parameter errors in power grids, focusing on statistical properties, error identification, computational efficiency, sensitivity analysis, and robust state estimation.

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