Lectures related to Signed measure

Quantifying Performance: Misclassification and F-Measure

Covers quantifying performance through true positives, false negatives, and false positives in machine learning.

Advanced Analysis II: Riemann Integrability and Jordan Measure

Explores Riemann integrability and Jordan measure, discussing the conditions for a set to be negligible.

Explains the evaluation of information retrieval models, including recall, precision, F-Measure, and the precision/recall tradeoff.

Evaluation Protocols

Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.

Distributions and Derivatives

Covers distributions, derivatives, convergence, and continuity criteria in function spaces.

Performance Evaluation: Bootstrap and Performance Metrics

Explores machine learning model evaluation using leave-one-out, bootstrap, and performance metrics like recall and precision.

Information Retrieval Basics: Document Frequency and Precision

Introduces information retrieval basics, emphasizing document frequency and precision in evaluating retrieval quality.

The Riesz-Kakutani Theorem

Explores the construction of measures, emphasizing positive functionals and their connection to the Riesz-Kakutani Theorem.

Riemann Integral: Properties and Characterization

Explores the properties and characterization of the Riemann integral on different sets and measurable sets.

Calculus of Variations: Principles and Applications

Explores the principles and applications of calculus of variations, focusing on uniform boundedness and equi-integrability of Carathéodory integrands.

Information Retrieval Basics: Document Length and Normalization

Explores document length, normalization, bias compensation, and retrieval model evaluation in information retrieval.

Advanced analysis II: properties and applications

Explores properties and applications of Riemann integrals, Jordan-measurable sets, and continuity in advanced analysis.

Performance Criteria: Confusion Matrix, Recall, Precision, Accuracy

Explores performance criteria in supervised learning, emphasizing precision, recall, and specificity in model evaluation.

Advanced analysis II

Covers Jordan-measurable sets, Riemann-integrability, and function continuity on compact sets.

Advanced Analysis II: Jordan-Measurable Functions

Explores Jordan-measurable functions and double integrals for volume calculations in 3D space.

Theoreus Chain Role: Lipschitz Sit

Covers the Theoreus Chain Role for Lipschitz functions and its practical applications.

Advanced analysis II: jordan-measurable sets

Explores Jordan-measurable sets and their properties, including volume calculations and change of variables in integrals.

Functional Calculus: Simple Functions

Covers the extension of functional calculus to simple functions and the concept of *-homomorphism.