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Postmortem Memory Analysis
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Related lectures (34)
Linear Regression: Basics and Estimation
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Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.
Understanding Data Attributes
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Covers the analysis of various data attributes and linear regression models.
Data-Driven Modeling: Regression
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Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Linear Regression: Basics
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Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Machine Learning Basics
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Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Back to Linear Regression
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Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.
Feature Engineering: Polynomial Regression
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Covers fitting linear regression on features of the original predictors for flexible feature representation.
Linear Regression Basics
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Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Gradient Descent: Linear Regression
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Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.
Linear Regression Basics
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Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Linear Regression Basics
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Introduces the basics of linear regression, covering OLS approach, residuals, hat matrix, and Gauss-Markov assumptions.
Design and Analysis of Experiments
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Covers the design and analysis of experiments, focusing on statistics for experimenters.
Instrumental Variables: Part 1
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Introduces instrumental variables to address endogeneity issues, using examples to illustrate practical applications and testing requirements.
Multilevel Models: Understanding Nested Data Structures
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Delves into multilevel models, emphasizing nested data structures and intra-class correlation, and explores random-intercept and random-slope models.
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