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Related lectures (29)
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Linear Binary Classification
Covers the extension of the 0-1 loss to real-valued score functions and logistic regression.
Non-Convex Optimization: Techniques and Applications
Covers non-convex optimization techniques and their applications in machine learning.
Gradient Descent
Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.
Hypothesis Space and Learning Task
Explores hypothesis space, supervised learning tasks, cost functions, and risk minimization in machine learning.
Mathematics of Data: Models and Estimators
Covers the Mathematics of Data, focusing on models, estimators, and practical issues in data analysis.
Learning the Kernel Solution: MGT-418 Tutorial
Explores learning the kernel solution in convex optimization, focusing on predicting outputs using a linear classifier and addressing possible numerical issues.
Statistical Learning Models: Risk and Empirical Risk Minimization
Covers statistical learning models, risk minimization, and empirical risk minimization with examples of maximum-likelihood estimators.
Logistic Regression: Part 1
Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.
Statistical Learning: Fundamentals
Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.
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