Machine learning

Algoritmos de estimación de distribuciones en estadística y aprendizaje automático

Distribution estimation algorithms in statistics and machine learning

Soledad Villar — Algebraic techniques for machine learning (Sept. 26, 2025)

Keynote: Machines, Learning, and Machine Learning - Dylan Beattie - NDC Porto 2025

Guide complet n8n et Claude : Créer tes AGENTS IA en 1 clic

Complete n8n and Claude guide: Create your AI AGENTS in 1 click

[ИАД, осень 2025] Математические методы прогнозирования. Лекция 5

[IAD, Fall 2025] Mathematical Forecasting Methods. Lecture 5

Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 4 - LLM Training

Investigación con consumidores sintéticos, RLMs, StreamingVLM

Research with synthetic consumers, RLMs, StreamingVLM

The Efficiency Equation: Leveraging AI Agents to Augment Human Labelers | Madhu Ramanathan, Meta

Machine Learning Explained: A Guide to ML, AI, & Deep Learning

From Benchmarks to Reality: Embedding HITL in Your MLOps Stack | Micaela Kaplan, HumanSignal

Autonomous MLOps Pipelines:Architecting Self-Healing, Drift Resistant Models | Kamal Bisht, Discover

Adversarial Threats Across the ML Lifecycle: A Red Team Perspective | Sanket Badhe, TikTok

L5 Gradient Vector Cost Functions MSE MAE LR Effect SGD Minibatch

L4 Gradient Descent GD Part 1

L3 Normal Equation RSS RSE R2

L2 Introduction to ML (continued), Simple & Multiple Linear Regression

Episode 9 - Inverse Reinforcement Learning

[ИАД, осень 2025] Байесовский выбор моделей. Лекция 6: Байесовская логистическая регрессия

[IAD, Fall 2025] Bayesian Model Selection. Lecture 6: Bayesian Logistic Regression

Week 4 Solve with us

ML Lagos Study Group Onboarding October Cohort

AI training data will never be fully synthetic [SPONSORED]

T3 Kaggle Assignment Introduction

[ИАД, осень 2025] Вероятностные тематические модели. Лекция 5

[IAD, autumn 2025] Probabilistic topic models. Lecture 5

[ИАД, осень 2025] Foundation models for spatial-time series. Занятие 4

[IAD, fall 2025] Foundation models for spatial-time series. Lesson 4

Using AI/ML To Find And Correlate IC Test Data

Stanford CS230 | Autumn 2025 | Lecture 3: Full Cycle of a DL project

Week 4

Tiny Recursive Model, Reducir memoria con AHN y estados de Markov, ICL en series temporales

Tiny Recursive Model, reducing memory with AHN and Markov states, ICL in time series

MLPT325 W4L1

Harnessing the Universal Geometry of Embeddings - Part1

Quantum Machine Learning Workshop - Day 1 (11.10.2025)

30 mathématiciens tentent de piéger l'IA : ils se font HUMILIER

30 mathematicians try to trap AI: they get HUMILIATED

Week 3 - Solve with us

[Paper Analysis] On the Theoretical Limitations of Embedding-Based Retrieval (Warning: Rant)

Kernelization of Natural Gradient Methods for PIML || Oct 10, 2025

Building An Automated Viral TikTok Account With AI

Unsupervised anomaly detection in multivariate time series - Laura BOGGIA

Uncertainty in AI driven physical simulation - Dimitrios TZIVRALIS - CEA LPTMS

Transformez votre chatbot en agent intelligent : découvrez OpenAI Agent Builder en action !

Turn your chatbot into an intelligent agent: discover OpenAI Agent Builder in action!

Machine learning of biological sequences - Jorge FERNANDEZ DE COSSIO DIAZ