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Machine Learning with Small Data Part 2 · LearnSpace
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Machine Learning with Small Data Part 2

Курс от Northeastern University
Уровень не указан≈ 11.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

By completing this course, you'll master building powerful machine learning systems that excel with limited data. You'll gain expertise in multi-task learning, meta-learning, and advanced data augmentation—from physics-based simulations to generative approaches—enabling models to adapt quickly and perform beyond their dataset size. What makes this course unique is its focus on cutting-edge 3D and generative technologies: Neural Radiance Fields (NeRF), diffusion models, and 3D Gaussian Splatting. Unlike traditional ML courses that assume abundant data, this program tackles real-world constraints while unlocking advanced capabilities in science, engineering, and creative industries. This course is primarily aimed at graduate students in computer science, engineering, or data science, along with industry professionals and researchers working with limited datasets who need to develop high-performance machine learning systems despite data constraints.

Навыки, которые вы освоите

Model OptimizationGenerative Model ArchitecturesComputer VisionMachine Learning Algorithms3D ModelingTransfer LearningSmall DataSimulationsImage AnalysisGenerative Adversarial Networks (GANs)AutoencodersDeep LearningGenerative AIMachine Learning Methods3D AssetsSimulation and Simulation SoftwareApplied Machine LearningModel TrainingComputer GraphicsMachine Learning

Программа курса

7 модулей · 97 учебных материалов

018 - Multi-Task Learning17 материалов

Getting Started

Course IntroductionЧтениеSyllabus - Machine Learning for Small Data Part 2ЧтениеAcademic IntegrityЧтение

Introduction to Multi-Task Learning

Introduction to Multi-Task LearningЧтение

Учитесь у экспертов

Sarah Ostadabbas

Преподаватель курса

Machine Learning with Small Data Part 2
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 11.6 ч

7 модулей

Язык: Английский

Субтитры: Венгерский

Часть программы вашего университета
Multi-Task LearningВидео
Examples of Multi-Task LearningЧтение
Why Multi-Task LearningЧтение
Key Challenges in MTLЧтение
Meta-Learning and Few-Shot Learning for Multi-Task LearningЧтение

CNPs and CNAPs

An Overview of Conditional Neural Processes (CNPs)ЧтениеConditional Neural Adaptive Processes (CNAPs)ЧтениеAdaptation Mechanisms of CNAPsЧтениеCNAPs Balances AdaptationЧтениеKey Extension in CNAPsЧтениеCNAPs in PracticeЧтениеAdaptation Network for CNAPsЧтение

Module 8 Assessment

Module 8 QuizЗадание
029 - Meta-Learning9 материалов

What is Meta-Learning?

What is Meta-Learning?ЧтениеMeta LearningВидеоModel-Agnostic Meta-Learning (MAML)ЧтениеPrototypical NetworksЧтение

Beyond Simple Meta-Learning

Beyond Simple Meta-Learning ЧтениеMathematical Formulation of Meta-LearningЧтениеMathematical Formulation of Transductive LearningЧтениеAn Overview of Some Vision Meta-DatasetsЧтение

Module 9 Assessment

Module 9 QuizЗадание
0310 - Learning With Data Augmentation: Data-Driven Simulation30 материалов

Generative Models for Data Augmentation

Introduction to Generative ModelsЧтениеLearning with Data Augmentation: Data-Driven SimulationВидеоLimitations of Generative Models for Data AugmentationЧтение

Generative Adversarial Networks (GANs), Its Variations and Applications

Generative Adversarial Networks (GANs)ЧтениеApplications of Generative ModelsЧтениеVanilla GANЧтениеConditional GAN (cGAN)ЧтениеDeep Convolutional GAN (DCGAN)ЧтениеWasserstein GAN (WGAN)ЧтениеCycleGANЧтениеProgressive Growing of GANs (PGGAN)ЧтениеInfoGANЧтениеBigGANЧтениеSuper-Resolution GAN (SRGAN)ЧтениеText-to-Image GANЧтение

Variational Autoencoders (VAEs), Its Variations and Applications

Autoencoder BasicsЧтениеVariational AutoencodersЧтениеProbabilistic Encoder, Reparameterization TrickЧтениеVAE Loss FunctionЧтениеVanilla VAE ЧтениеBeta-VAE ЧтениеConditional VAE

Flow-Based & Diffusion Models

Flow-Based ModelsЧтениеAdvancements in Flow-Based Generative Models Part 1ЧтениеAdvancements in Flow-Based Generative Models Part 2ЧтениеAdvancements in Flow-Based Generative Models Part 3ЧтениеDiffusion ModelsЧтениеComparative Summary of Generative ModelsЧтение

Module 10 Assessment

Module 10 QuizЗадание
0411 - Learning With Data Augmentation: Physics-Based Simulation12 материалов

Physics-Based Simulation

Introduction to Physics-Based SimulationВидеоPhysics-Based SimulationЧтениеGeoNet: Using Physical Relationship in Image Formation Чтение

Avatar-Based Human Body Simulation

Avatar-Based SimulationЧтениеScanAvaЧтениеSkinned Multi-Person Linear Model (SMPL)ЧтениеSkinned Multi-Person Linear Model (SMPL) Part 2Чтение

Equation-Based Simulation

Governing Equations in Physics-Based SimulationЧтениеPartial Differential Equations (PDEs)ЧтениеNumerical Methods for Solving PDEsЧтениеComparison of MethodsЧтение

Module 11 Assessment

Module 11 QuizЗадание
0512 - Neural Radiance Fields (NeRF)8 материалов

Neural Radiance Fields (NeRF)

Introducing Neural Radiance Fields (NeRF)ЧтениеNeRFВидеоVolume RenderingЧтениеDiscrete Approximation in Volume RenderingЧтениеNeRF Network StructureЧтение

Neural Reflectance and Visibility Fields for Relighting and View Synthesis (NeRV)

NeRF Extension: NeRVЧтениеNeRF vs. NeRVЧтение

Module 12 Assessment

Module 12 QuizЗадание
0613 - Diffusion Models13 материалов

Diffusion Models

Introduction to Diffusion ModelsВидеоForward and Reverse Diffusion in DenoisingЧтениеComponents of Denoising Diffusion ModelsЧтениеLoss Decomposition and Noise LevelsЧтениеVariance Schedule and Training StepsЧтениеThe Rapidly Evolving Field of DDMЧтениеFoundational Understanding of Diffusion ModelsЧтениеKey Model Variants and ImprovementsЧтениеGuided and Conditional GenerationЧтениеVideo Diffusion Models IЧтениеVideo Diffusion Models IIЧтениеVideo Diffusion Models IIIЧтение

Module 13 Assessment

Module 13 QuizЗадание
0714 - 3D Gaussian Splatting8 материалов

3D Gaussian Splatting

3D Gaussian SpattingВидеоIntroducing 3D Gaussian SpattingЧтениеIsotropic & Anisotropic in 3DGSЧтениеKey Concepts & Methodology in 3DGSЧтениеOptimization & Training in 3DGSЧтениеNeRF versus 3DGSЧтение

Module 14 Assessment

Module 14 QuizЗадание

Course Conclusion

Congratulations! Чтение
Чтение
VQ-VAEЧтение