Distributed Intelligent Systems: Energy Efficiency in Wireless Sensor Networks

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 1 of 4

Distributed Intelligent Systems: Acoustic Field Estimation

Explores acoustic field estimation using wireless sensor networks and discusses challenges in implementing AI techniques in real systems.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Clustering: Unsupervised Learning

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

Graph Coloring II

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Clustering Methods and Dimensionality Reduction

Covers clustering methods and dimensionality reduction techniques.

Clustering Methods

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.