AI & Machine Learning 2025

Scientific sessions

Scientific sessions · Oct 20–22, 2025 · Tokyo, Japan

We invite original contributions across AI and machine learning theory, applications, and interdisciplinary work. Present your research to an international audience and engage in structured scientific sessions.

Machine Learning (ML)

Machine Learning is a branch of Artificial Intelligence where systems learn from data and improve over time without being explicitly programmed. It involves training models using algorithms to recognize patterns and make decisions based on data. ML techniques are commonly used for tasks like classification, regression, and clustering, with applications ranging from spam detection to predictive analytics in finance and healthcare. It includes supervised, unsupervised, and reinforcement learning, each serving different types of problems. ML has become essential in industries like marketing, robotics, and healthcare due to its ability to derive insights from large datasets.

Deep Learning (DL)

Deep Learning is a specialized subfield of Machine Learning that uses neural networks with many layers to model complex patterns in data. It excels at tasks such as image and speech recognition, where traditional machine learning methods may struggle. Deep learning models automatically extract features from raw data (like pixels in images), eliminating the need for manual feature engineering. While it requires large amounts of data and computing power, it has achieved state-of-the-art results in fields like computer vision, natural language processing, and autonomous vehicles. Deep learning continues to drive advancements in AI technologies due to its high accuracy and scalability.

Conference schedule · Topics on the overview · Back to conference home

Scientific sessions

Machine Learning & Deep Learning

Neural Networks & Computer Vision

Natural Language Processing (NLP)

Chatbots & Generative AI

Reinforcement Learning & Predictive Analytics

AI Ethics & AI Algorithms

AI in Healthcare

Autonomous Systems

Supervised Learning & Unsupervised Learning

AI in Robotics & Edge AI

AI in Marketing & AI in Finance

Transfer Learning & AI Bias

AI Transparency & Decision Trees

Sentiment Analysis & Federated Learning

AI in Autonomous Vehicles

AI in Cybersecurity

Convolutional Neural Networks (CNN)

Reinforcement Learning Algorithms

Data Preprocessing & Data Augmentation

Support Vector Machines (SVM)

Random Forests & Clustering

Feature Engineering & Dimensionality Reduction

K-Nearest Neighbors (KNN)

Gradient Boosting Machines (GBM)

Deep Reinforcement Learning

Generative Adversarial Networks (GANs)

Hyperparameter Tuning & Hyperparameter Optimization

Principal Component Analysis (PCA)

Anomaly Detection & AutoML

Multi-task Learning & Algorithmic Trading

AI-Powered Tools & Facial Recognition