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Machine Learning with Small Data Part 1 · LearnSpace
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Machine Learning with Small Data Part 1

Курс от Northeastern University
Уровень не указан≈ 14.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course addresses the challenge of machine learning (ML) in the context of small datasets, a significant issue due to ML's increasing data demands. Despite ML's success in various fields, many areas can't provide large labeled datasets because of costs, privacy, or security laws. As big data becomes standard, efficiently learning from smaller datasets is crucial. This course, ideal for graduate students with some ML experience, focuses on modern deep learning techniques for small data applications relevant in healthcare, military, and various industry sectors. Prerequisites include ML familiarity and Python proficiency. Deep learning experience is not necessary but beneficial.

Навыки, которые вы освоите

Small DataMachine LearningTransfer LearningFine-tuningUnsupervised LearningMachine Learning MethodsData SynthesisModel TrainingSupervised LearningDeep LearningApplied Machine Learning

Программа курса

7 модулей · 98 учебных материалов

011 - Introduction to Machine Learning with Small Data16 материалов

Welcome Module

Course OverviewЧтениеSyllabus - Machine Learning for Small DataЧтениеAcademic IntegrityЧтение

Data Matters

Data Matters—Especially for Deep LearningЧтение

Учитесь у экспертов

Sarah Ostadabbas

Преподаватель курса

Machine Learning with Small Data Part 1
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 14.4 ч

7 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Казахский, Венгерский

Часть программы вашего университета
Data MattersВидео
Data-Parameters-Power Scaling in AI ModelЧтение
Exponential Growth of Training DataЧтение
Exponential Growth of Model ComplexityЧтение
Exponential Growth in Computational ResourcesЧтение
The Scale Paradox: When Smaller ML Models Outperform GiantsЧтение
Large Datasets for Deep LearningЧтение

Small Data

What is Small Data?Чтение

Installing PyTorch

Installing PyTorchЧтениеSetting Up Your Local EnvironmentВидео

Reflection

Large vs. Small Datasets in Machine LearningЧтение

Module 1 Assessment

Module 1 QuizЗадание
022 - Formal Learning Theory23 материалов

Successful Ingredients of Deep Learning (DL)

Ingredients RelationshipЧтениеMachine Learning Model PerformanceВидеоComputing Power: Growth Beyond Moore’s LawЧтениеScaling LawsЧтение Learning CurvesЧтение

Model Performance and Capacity

Model Capacity Required to Fit DataЧтениеModel Performance and Dataset SizeЧтениеModel Performance and Model CapacityЧтение

Balancing Complexity and Data via Bias-Variance Trade-Off

Bias-Variance Trade-OffЧтениеFrom a Linear Algebra PerspectiveЧтениеUnderdetermined Problems and Overparameterized ModelsЧтениеRevisiting Bias-Variance with Double DescentЧтение

Learning Paradigms

Comparison of Learning ParadigmsЧтениеA Learning MachineЧтениеExamples of Learning MachinesВнешний инструментHow Do We Characterize Model Complexity?Чтение

Vapnik–Chervonenkis (VC) Dimension

Vapnik–Chervonenkis (VC) Dimension - ShatteringЧтениеNotions of VC DimensionЧтениеExamples of Shattering and VC DimensionЧтениеVC Dimension in Neural NetworksЧтениеCalculating the VC Dimension of SVM ModelsЗаданиеResourcesЧтение

Module 2 Assessment

Module 2 QuizЗадание
033 - Transfer Learning17 материалов

Data-efficient Machine Learning

Data-efficient Machine LearningЧтениеTransfer LearningВидеоLeveraging Pre-trained Models for Efficient Machine LearningЧтениеVanilla Transfer Learning ЧтениеTypes of Transfer LearningЧтение

Transductive & Inductive Transfer Learning

Transductive Transfer Learning AlgorithmsЧтениеInductive Transfer Learning AlgorithmsЧтениеTransductive Examples IЧтениеTransductive Examples IIЧтениеTransductive Examples IIIЧтениеInductive ExamplesЧтение

Multi-Task Learning vs Meta Learning

Multi-Task Learning & Meta-LearningЧтениеSynthetic Data AugmentationЧтениеData-Driven SimulationЧтениеPhysics-Based SimulationЧтениеPhysics-Based Simulation ExamplesЧтение

Module Assessment

Module 3 QuizЗадание
044 - Domain Adaptation12 материалов

Domain Adaptation

Domain Adaptation: BackgroundЧтениеDomain AdaptationВидеоUnsupervised, Semi-Supervised & SupervisedЧтение

Deep Domain Confusion

Deep Domain ConfusionЧтениеRelated Work Based on DDCЧтениеDeep Domain Confusion ArchitectureЧтениеImplementation & ArchitectureЧтениеMathematical FormulationЧтение

Datasets

An Example Dataset: Office-31ЧтениеAn Example DDC ExperimentЧтение

DDC Transfer Learning Practice

Transfer Learning Practice ActivityЧтение

Module 4 Assessment

Module 4 QuizЗадание
055 - Learning with Weak Supervision10 материалов

Weak Supervision

What is Weak Supervision?ВидеоTypes of Weak SupervisionЧтениеSemi-Supervised LearningЧтениеSelf-Supervised LearningЧтениеActive LearningЧтение

Applications & Case Studies

Applications of Weak SupervisionЧтениеCase Study: Medical ImagingЧтениеCase Study: Autonomous DrivingЧтениеCase Study: Natural Language ProcessingЧтение

Module 5 Assessment

Module 5 QuizЗадание
066 - Zero-Shot Learning11 материалов

Zero-Shot Learning

Introduction to Zero-Shot LearningЧтениеZSL: Notation and Problem SetupЧтениеLearning a Linear Predictor for Seen ClassesЧтениеProblem Extension for ZSL: From Seen to Unseen ClassesЧтениеAn Embarrassingly Simple Approach to ZSLЧтениеZSL with Generative ModelsЧтение

Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL)ЧтениеGeneralized Zero-Shot LearningВидеоZero-Shot Learning: Semantic AutoencodersЧтениеGeneralized ZSL With Generative ModelsЧтение

Module 6 Assessment

Module 6 QuizЗадание
077 - Few-Shot Learning9 материалов

Few-Shot Learning

Introduction to Few-Shot LearningВидеоWhat is Few-Shot Learning?Чтение

One Shot Learning

Introduction to One-Shot LearningЧтениеMatching Networks: An Approach to One-Shot LearningЧтениеTraining Matching NetworksЧтение

Improving and Enhancing FSL

Improving Few-Shot Visual ClassificationЧтениеEnhancing Few-Shot Image Classification With Unlabeled ExamplesЧтение

Module 7 Assessment

Module 7 QuizЗадание

Course Conclusion

CongratulationsЧтение