Resource-Aware Machine Learning in the IoT Era

Related lectures (31)

Page 3 of 4

Introduces the basics of machine learning, covering supervised classification, decision boundaries, and polynomial curve fitting.

Covers clustering, semi-supervised clustering, and binary classification formalization, along with various classification techniques.

Explores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Explores Support Vector Machines theory, parameters, uniqueness, and applications in machine learning.

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Covers the basics of machine learning, focusing on supervised learning algorithms and real-world applications.

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

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