⚡️ Graph Machine Learning Бесплатный продвинутый курс: Машинное обучение на графах. Курс регулярно дополняется практическими задачками и слайдами. Автор Ксавье Брессон - профессор национального университета Сингапура. ▪Введение ▪Погружение в графы - Lab1: Generate LFR social networks https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code01.ipynb - Lab2: Visualize spectrum of point cloud & grid https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code02.ipynb - Lab3/4: Graph construction for two-moon & text documents https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code03.ipynb https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code04.ipynb ▪Кластеризация графов - Lab1: k-means https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code01.ipynb https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code02.ipynb - Lab2: Metis https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code03.ipynb - Lab3/4: NCut/PCut https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code04.ipynb https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code05.ipynb - Lab5: Louvain https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code06.ipynb https://pic.twitter.com/vSXCx364pe ▪Лекции 4 Graph SVM - Lab1: Standard/Linear SVM https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code01.ipynb - Lab2: Soft-Margin SVM https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code02.ipynb - Lab3: Kernel/Non-Linear SVM https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code03.ipynb - Lab4: Graph SVM https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code04.ipynb Инструкции по запуску: https://storage.googleapis.com/xavierbresson/lectures/CS6208/running_notebooks.pdf 💡 Github @machinelearning