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Support Vector Machine and Logistic Regression
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Related lectures (34)
Logistic Regression: Cost Functions & Optimization
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Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.
Logistic Regression: Probabilistic Interpretation
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Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.
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.
Supervised Learning Fundamentals
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Introduces the fundamentals of supervised learning, including loss functions and probability distributions.
Linear Models: Part 2
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Covers linear models, binary and multi-class classification, and logistic regression with practical examples.
Supervised Learning Essentials
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Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Linear Models: Classification Basics
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Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.
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 Models: Continued
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Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Linear Regression: Beyond the Basics
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Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.
Probability & Stochastic Processes
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Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.
Ensemble Methods: Random Forest
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Explores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.
Probability Theory: Random Variables and Independence
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Explores discrete and continuous random variables, independence, and probability functions.
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