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Lecture
Linear Models for Classification: Part 3
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Related lectures (55)
Linear Models: Part 1
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Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Linear Regression and Logistic Regression
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Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Linear Models: Basics
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Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Logistic Regression: Cost Functions & Optimization
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Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.
Supervised Learning in Asset Pricing
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Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.
Linear Models & k-NN
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Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.
Supervised Learning: Classification Algorithms
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Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
Linear and Logistic Regression
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Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Linear Models: Part 2
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Covers linear models, binary and multi-class classification, and logistic regression with practical examples.
Linear Models: Continued
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Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.
Document Analysis: Topic Modeling
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Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Machine Learning Fundamentals: Regularization and Cross-validation
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Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.
Supervised Learning Fundamentals
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Introduces the fundamentals of supervised learning, including loss functions and probability distributions.
Logistic Regression: Classification
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Covers supervised learning, classification using logistic regression, and challenges in optimization.
Polynomial Regression: Overview
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Covers polynomial regression, flexibility impact, and underfitting vs overfitting.
Untitled
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Binary Response: Link Functions
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Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.
Kernel Regression: K-nearest Neighbors
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Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.
Modern Regression: Statistical Models and Data Analysis
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Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.
Neural Networks: Basics and Applications
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Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.
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