Francis Bach
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Featured books
Representative editions for works actually authored by this person.
- Image source: Open LibraryFO
Foundations of machine learning
cover - Image source: Open LibraryRL
Reinforcement Learning
cover - Image source: Open LibraryML
Machine Learning for Data Streams
cover - Image source: Open LibraryDL
Deep Learning
cover - Image source: Open LibraryIT
Introduction to Machine Learning
cover - Image source: Open LibraryB
Boosting
cover - Image source: Open LibraryPG
Probabilistic Graphical Models
cover - Image source: Open LibraryMD
Minimum Description Length Principle
cover - Image source: Open LibraryTM
The minimum description length principle
cover - Image source: Open LibraryGP
Gaussian processes for machine learning
cover - Image source: Open LibraryLK
Learning Kernel Classifiers
cover - Image source: Open LibraryLI
Learning in graphical models
cover - MLMachine Learning in Non-Station...Francis Bach
Machine Learning in Non-Stationary Environments
no cover - LWLearning with Kernels - Support...Francis Bach
Learning with Kernels - Support Vector Machines, Regularization, Optimization, and Beyond
no cover - SMSparse Modeling for Image and V...Francis Bach
Sparse Modeling for Image and Vision Processing
no cover - LWLearning with Submodular FunctionsFrancis Bach
Learning with Submodular Functions
no cover - OWOptimization with Sparsity-Indu...Francis Bach
Optimization with Sparsity-Inducing Penalties
no cover - POPrinciples of Data MiningFrancis Bach
Principles of Data Mining
no cover
Works in catalog
Quick navigation into the work-level grouping pages behind the featured books.
- Open Work
Foundations of machine learning
- Open Work
Reinforcement Learning
- Open Work
Machine Learning for Data Streams
- Open Work
Deep Learning
- Open Work
Introduction to Machine Learning
- Open Work
Boosting
- Open Work
Probabilistic Graphical Models
- Open Work
Minimum Description Length Principle
- Open Work
The minimum description length principle
- Open Work
Gaussian processes for machine learning
- Open Work
Learning Kernel Classifiers
- Open Work
Learning in graphical models
- Open Work
Machine Learning in Non-Stationary Environments
- Open Work
Learning with Kernels - Support Vector Machines, Regularization, Optimization, and Beyond
- Open Work
Sparse Modeling for Image and Vision Processing
- Open Work
Learning with Submodular Functions
- Open Work
Optimization with Sparsity-Inducing Penalties
- Open Work
Principles of Data Mining