A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)

螺旋抗熵系统

螺旋抗熵理论

AAAI 2018 - Unsupervised video summarization with deep reinforcement learning (Theano)

pyMCR: Multivariate Curve Resolution for Python

Self-organizing maps in Go

This project provides GOLang implementation of Neuro-Evolution of Augmenting Topologies (NEAT) with Novelty Search optimization aimed to solve deceptive tasks with strong local optima

The implementation of evolvable-substrate HyperNEAT algorithm in GO language. ES-HyperNEAT is an extension of the original HyperNEAT method for evolving large-scale artificial neural networks.

Stanford Online course STATSX0001 "Statistical Learning" follows closely the sequence of chapters in the course text "An Introduction to Statistical Learning, with Applications in R" (James, Witten, Hastie, Tibshirani - Springer 2013). Trevor Hastie Professor of Statistics and of Biomedical Data Sciences, Stanford University, and Robert Tibshira…

unsupervised learning of natural images -- à la SparseNet.

GRASPED: A GRU-AE Network based Multi-perspective Business Process Anomaly Detection Model

This repository contains practical implementations of core machine learning algorithms and techniques, created for learning and practice purposes.

Reproducible Python tutorials and benchmarks for outlier detection using statistical methods, machine learning, ensembles, autoencoders, tests, exercises, and CI.

Adaptive Common Spatio-Temporal Pattern (ACSTP) for Event-Related Potential (ERP) analysis.

This repository contains machine learning programs in the Python programming language.

It consists of basic concepts of Machine-Learning with its algorithms.

Applying pre-trained CNN and clustering algorithms on recognizing people in videos

Complete lecture slides for Machine Learning (ES-442) at GIK Institute, Fall 2025. Covers Supervised Learning (Decision Trees, SVM, Neural Networks), Unsupervised Learning (Clustering, SOM), and Reinforcement Learning (MDPs, Q-Learning, Deep RL).

Unsupervised ensemble learning methods for time series forecasting. Bootstrap aggregating (bagging) for double-seasonal time series forecasting and its ensembles.