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Hypothesis Space and Learning Task
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Related lectures (32)
Machine Learning Fundamentals
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Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Supervised Learning: Formalization and Cost Functions
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Covers the formalism for supervised learning and decision functions in classification problems.
Machine Learning Fundamentals
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Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.
Supervised Learning Fundamentals
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Introduces the fundamentals of supervised learning, including loss functions and probability distributions.
Supervised Learning in Asset Pricing
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Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.
Image Classification: Decision Trees & Random Forests
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Explores image classification using decision trees and random forests to reduce variance and improve model robustness.
Financial Time Series Analysis
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Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Machine Learning Basics
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Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Linear Regression: Basics and Gradient Descent
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Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.
Introduction to Machine Learning: Course Overview and Basics
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Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.
Machine Learning and Modern AI: SWOT Analysis
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Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.
Supervised Learning: Regression Methods
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Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.
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