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Lecture
Machine Learning Basics: Supervised and Unsupervised Learning
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Related lectures (32)
Regression Trees and Ensemble Methods in Machine Learning
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Machine Learning Basics: Supervised Learning
Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.
Supervised Learning: Regression Methods
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Supervised Learning: Classification Algorithms
Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
Overfitting in Supervised Learning: Case Studies and Techniques
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Supervised Learning: k-NN and Decision Trees
Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.
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Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Decision Trees: Induction & Attributes
Explores decision trees, attribute selection, bias-variance tradeoff, and ensemble methods in machine learning.
Supervised Learning Fundamentals
Introduces the fundamentals of supervised learning, including loss functions and probability distributions.
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Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.
Linear Regression and Logistic Regression
Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Decision Trees and Random Forests: Concepts and Applications
Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.
Supervised Learning: Linear Regression
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Linear Regression: Basics and Gradient Descent
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Introduction to Machine Learning: Linear Models
Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.
Supervised Learning in Asset Pricing
Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.
Machine Learning Fundamentals
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Linear and Logistic Regression
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Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.
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Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
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