Formal & Physical Sciences AI — Models & ResearchENMLT | Week-8 | Naive Bayes Algorithm | Live SessionMLT cs2007August 6, 2026 132 min★ ★ ★ ★ ☆ 4/5Naive BayesGenerative ModelsMachine Learning
Formal & Physical Sciences AI — Models & ResearchENOptimal Inference Schedules for Masked Diffusion ModelsJerry LiAugust 5, 2026 39 min★ ★ ★ ★ ☆ 4/5Masked Diffusion ModelsInference ParallelizationInformation Theory
Formal & Physical Sciences AI — Models & ResearchENA new class of algorithms for trajectory inferenceAram-Alexandre PooladianAugust 5, 2026 42 min★ ★ ★ ★ ☆ 4/5Trajectory InferenceProbability SplinesGenerative Modeling
Applied Sciences & Engineering AI — Models & ResearchENHybridization of Neural Networks and Numerical Solvers in JAX with Differentiable PhysicsFelix Köhler (Ceyron)August 4, 2026 81 min★ ★ ★ ★ ★ 5/5JAXDifferentiable ProgrammingNeural Networks
Formal & Physical Sciences AI — Models & ResearchENStanford CS329A Self-Improving AI Agents | Part 9 | Future Research AreasAakanksha Chowdhery and Azalia MirhoseiniAugust 3, 2026 67 min★ ★ ★ ★ ☆ 4/5Self-Improving AIMulti-Agent Fine-TuningVerification
Formal & Physical Sciences AI — Models & ResearchENStanford CS329A Self-Improving AI Agents | Part 7 | Self-Improvement and Deep Research AgentsAakanksha ChowdheryAugust 3, 2026 72 min★ ★ ★ ★ ☆ 4/5Self-Improving AISearchAlphaCode
Formal & Physical Sciences AI — Models & ResearchENStanford CS329A Self-Improving AI Agents | Part 6 | Train Time Scaling/Scaling RLAakanksha ChowdheryAugust 3, 2026 72 min★ ★ ★ ★ ☆ 4/5Reinforcement LearningTrain-Time ScalingReasoning
Formal & Physical Sciences AI — Models & ResearchENStanford CS329A Self-Improving AI Agents | Part 5 | Planning and Multi-Step ReasoningAzalia MirhoseiniAugust 3, 2026 74 min★ ★ ★ ★ ☆ 4/5AI AgentsPlanningMulti-Step Reasoning
Formal & Physical Sciences AI — Models & ResearchENStanford CS329A Self-Improving AI Agents | Part 4 | Learning from Feedback with Tools/CodeAakanksha ChowdheryAugust 3, 2026 71 min★ ★ ★ ★ ☆ 4/5AI AgentsReActReinforcement Learning
Formal & Physical Sciences AI — Models & ResearchENStanford CS329A Self-Improving AI Agents | Part 3 | Robust VerificationAzalia MirhoseiniAugust 3, 2026 72 min★ ★ ★ ★ ☆ 4/5VerificationLLMReward Models
Formal & Physical Sciences AI — Models & ResearchENEfficient Reinforcement Learning for Diffusion ModelsYongxin ChenAugust 3, 2026 46 min★ ★ ★ ★ ☆ 4/5Reinforcement LearningDiffusion ModelsEfficiency
Applied Sciences & Engineering AI — Models & ResearchENArchitectures for an AI agent for route planning and Next-Latent PredictionParag (San Diego Machine Learning)August 2, 2026 102 min★ ★ ★ ★ ☆ 4/5AI AgentRoute PlanningMathematical Optimization
Formal & Physical Sciences AI — Models & ResearchENMLT | Week-7 | Solve with us-Decision Tree and K-NNMachine Learning TechniquesAugust 1, 2026 110 min★ ★ ★ ★ ☆ 4/5K-NNDecision TreesClassification
Formal & Physical Sciences AI — Models & ResearchENStanford CS229 Machine Learning | Spring 2026 | Lecture 9: K-Means and GMM (non-EM)Chris Ré, Tengyu MaJuly 31, 2026 76 min★ ★ ★ ★ ☆ 4/5K-MeansGaussian Mixture ModelsUnsupervised Learning
Formal & Physical Sciences AI — Models & ResearchENStanford CS229 Machine Learning | Spring 2026 | Lecture 20: GMM (EM), PCAStanford OnlineJuly 31, 2026 78 min★ ★ ★ ★ ☆ 4/5Machine LearningPolicy GradientReinforcement Learning
Formal & Physical Sciences AI — Models & ResearchENStanford CS229 Machine Learning | Spring 2026 | Lecture 18: GMM (EM), PCAChris Ré, Tengyu MaJuly 31, 2026 76 min★ ★ ★ ★ ☆ 4/5Reinforcement LearningPolicy GradientMDP
Formal & Physical Sciences AI — Models & ResearchENStanford CS229 Machine Learning | Spring 2026 | Lecture 13: LLMs, Next-Word Prediction LossStanford OnlineJuly 31, 2026 60 min★ ★ ★ ★ ☆ 4/5Machine LearningLarge Language ModelsRepresentation Learning
Formal & Physical Sciences AI — Models & ResearchENStanford CS229 Machine Learning | Spring 2026 | Lecture 10: GMM (EM), PCAChris RéJuly 31, 2026 80 min★ ★ ★ ★ ★ 5/5GMMEM AlgorithmPCA
Formal & Physical Sciences AI — Models & ResearchENStanford CS229 Machine Learning | Spring 2026 | Lecture 6: Dataset Split, ML AdviceChris Ré, Tengyu MaJuly 30, 2026 78 min★ ★ ★ ★ ☆ 4/5Bias-Variance TradeoffOverfittingRegularization
Formal & Physical Sciences AI — Models & ResearchENStanford CS229 Machine Learning | Spring 2026 | Lecture 5: Gaussian Discriminant AnalysisChris RéJuly 30, 2026 81 min★ ★ ★ ★ ☆ 4/5Gaussian Discriminant AnalysisGenerative ModelsMachine Learning
Formal & Physical Sciences AI — Models & ResearchENLec 18: First steps in performanceDr. Satyajit Das and Prof. Satyadhyan ChickerurJuly 30, 2026 23 min★ ★ ★ ★ ☆ 4/5PyTorchMixed PrecisionData Types
Formal & Physical Sciences AI — Models & ResearchENLec 17: Data loaded & Feeding the model efficientlyNPTEL IIT GuwahatiJuly 30, 2026 26 min★ ★ ★ ★ ☆ 4/5Data LoadingPyTorchDeep Learning
Formal & Physical Sciences AI — Models & ResearchENStanford CS229 Machine Learning | Spring 2026 | Lecture 4: Exponential Family, GLMs ClassificationStanford OnlineJuly 29, 2026 74 min★ ★ ★ ★ ★ 5/5Machine LearningExponential FamilyGeneralized Linear Models
Formal & Physical Sciences AI — Models & ResearchENStanford CS229 Machine Learning | Spring 2026 | Lecture 3: Weighted Least SquaresChris Ré and Tengyu MaJuly 29, 2026 62 min★ ★ ★ ★ ☆ 4/5Machine LearningLogistic RegressionMaximum Likelihood
Formal & Physical Sciences AI — Models & ResearchENStanford CS229 Machine Learning | Spring 2026 | Lecture 2: Supervised Learning SetupChris RéJuly 29, 2026 78 min★ ★ ★ ★ ☆ 4/5Supervised LearningLinear RegressionGradient Descent
Formal & Physical Sciences AI — Models & ResearchENStanford CS229 Machine Learning | Spring 2026 | Lecture 1: IntroductionTengyu MaJuly 29, 2026 36 min★ ★ ★ ★ ☆ 4/5Machine LearningStanfordCS229
Applied Sciences & Engineering AI — Models & ResearchENHow to Train Open Models with RL on Prime Intellect | Nemotron LabsNVIDIA DeveloperJuly 29, 2026 45 min★ ★ ★ ★ ☆ 4/5Reinforcement LearningOpen ModelsPrime Intellect
Formal & Physical Sciences AI — Models & ResearchENWeek 5 &6 - Solve with us (Additional session)MLT cs2007July 28, 2026 121 min★ ★ ★ ☆ ☆ 3/5Linear RegressionGradient DescentKernel Methods
Formal & Physical Sciences AI — Models & ResearchENMLT | Week-6 | Summary SessionMLT cs2007July 25, 2026 110 min★ ★ ★ ★ ☆ 4/5Linear RegressionRidge RegressionMachine Learning
Formal & Physical Sciences AI — Models & ResearchENEverything About Machine Learning Explained Slowly (For Sleep)Cosmo ExplainsJuly 25, 2026 127 min★ ★ ★ ★ ☆ 4/5Machine LearningHistory of AINeural Networks
Formal & Physical Sciences AI — Models & ResearchENYash Sarrof: Length Generalization of Transformers with a Growing Test time AlphabetYash SarrofJuly 23, 2026 44 min★ ★ ★ ★ ☆ 4/5Length GeneralizationTransformersRASP
Formal & Physical Sciences AI — Models & ResearchENPeter Shaw: Asymptotically Optimal Description Length Objectives for TransformersPeter ShawJuly 23, 2026 42 min★ ★ ★ ★ ☆ 4/5TransformersMinimum Description LengthAlgorithmic Information Theory
Formal & Physical Sciences AI — Models & ResearchENMLT | Week-5 | Summary SessionMayur GundalJuly 23, 2026 139 min★ ★ ★ ☆ ☆ 3/5Machine LearningLinear RegressionClassification
Formal & Physical Sciences AI — Models & ResearchENIdan Attias: On the Hardness of Learning Regular ExpressionsIdan AttiasJuly 23, 2026 56 min★ ★ ★ ★ ☆ 4/5Regular ExpressionsComputational Learning TheoryPAC Learning
Formal & Physical Sciences AI — Models & ResearchENIs Fine-Tuning Still Needed? LLMs, RAG, & LoRAMartin KeenJuly 21, 2026 10 min★ ★ ★ ★ ☆ 4/5Fine-TuningRAGLoRA
Formal & Physical Sciences AI — Models & ResearchENMLT | Week-3 and 4 | Revision Session-2MLT cs2007July 18, 2026 217 min★ ★ ★ ☆ ☆ 3/5K-MeansClusteringMachine Learning
Formal & Physical Sciences AI — Models & ResearchENLearning machinesPiyush SrivastavaJuly 18, 2026 82 min★ ★ ★ ★ ☆ 4/5Machine LearningLinear RegressionGradient Descent
Formal & Physical Sciences AI — Models & ResearchENHackathon 3 - ML-Enhanced Analysis of EELS - SlautinMachine Learning in the NanoworldJuly 18, 2026 13 min★ ★ ★ ☆ ☆ 3/5EELSVariational AutoencoderMachine Learning
Applied Sciences & Engineering AI — Models & ResearchENHackathon 2 - Data curation and unsupervised analysis of the diffraction data - HoustonMachine Learning in the NanoworldJuly 18, 2026 17 min★ ★ ★ ★ ☆ 4/54D-STEMDigital TwinData Analysis
Formal & Physical Sciences AI — Models & ResearchENDay 5 - Reward Functions for Decision Making - KalininSergei KalininJuly 18, 2026 49 min★ ★ ★ ★ ☆ 4/5Bayesian OptimizationGaussian ProcessesAutomated Experimentation