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Adaptive Computation and Machine Learning series

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Foundations of Computer Vision

Deep Learning

Probabilistic Machine Learning: Advanced Topics

Introduction To Machine Learning, Fourth Edition

Probabilistic Machine Learning: Advanced Topics

Probabilistic Graphical Models: Principles and Techniques

Knowledge Graphs: Fundamentals, Techniques, And Applications

Machine Learning: A Probabilistic Perspective

Reinforcement Learning, Second Edition: An Introduction

Elements Of Causal Inference: Foundations And Learning Algorithms

Introduction To Natural Language Processing

Fairness and Machine Learning: Limitations and Opportunities

Introduction To Statistical Relational Learning

Introduction To Online Convex Optimization, Second Edition

Learning Theory from First Principles

Machine Learning From Weak Supervision: An Empirical Risk Minimization Approach

Learning Kernel Classifiers: Theory and Algorithms

Foundations Of Machine Learning, Second Edition

Machine Learning for Data Streams: with Practical Examples in MOA

Learning With Kernels: Support Vector Machines, Regularization, Optimization, And Beyond

Machine Learning, Second Edition: A Probabilistic Perspective

Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making

Gaussian Processes For Machine Learning