Graph Machine Learning

Related lectures (30)

Page 3 of 3

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Explores trend analysis in Excel using rent data from 1995 to 2023.

Explores Prim's algorithm for minimum spanning trees and introduces the Traveling Salesman Problem.

Explores predicting reaction yields with deep learning models and the importance of high-quality data sets in chemistry.

Covers matrices, linear applications, vector spaces, and bijective functions.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Explores real functions of two real variables, illustrating domains, images, and graphs.

Discusses the graph of a relation and the concept of separation.

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.