Lecture

Statistical Inference and Machine Learning

Related lectures (74)
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Explores SVM model storage, memory usage, training time complexity, and energy consumption estimation.
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Covers the Perceptron model, SGD, and Fisher's Linear Discriminant in binary classification.
Support Vector Machines: Soft Margin
Explores Support Vector Machines with a focus on soft margin and multiclass classification using binary classifiers.
Support Vector Machines: Basics and Applications
Covers the basics of Support Vector Machines, including linear separability, hyperplanes, margins, and non-linear SVM with kernels.
Support Vector Machines: SVMs
Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.
SVM and Multiclass Classification
Covers SVM and multiclass classification using one-vs-all and one-vs-one approaches.
Multi-Class Classification: Approaches and Boundaries
Explains the strategies for multi-class classification and the concept of decision boundaries.
Support Vector Machines: Exercises Solutions
Covers solutions to SVM exercises, discussing optimality conditions, decision functions, and parameter impacts.
Multiclass SVM
Covers the use of Support Vector Machines for multi-class classification and the importance of support vectors in tightening classification boundaries.
Scientific Machine Learning: Optimal Errors
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