| 1 |
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A Probabilistic Theory of Pattern Recognition
by
3.92 avg rating — 13 ratings
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| 2 |
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Foundations of Machine Learning
by
4.21 avg rating — 94 ratings
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| 3 |
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Understanding Machine Learning
by
4.21 avg rating — 131 ratings
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| 3 |
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Geometric Modeling in Probability and Statistics
by
4.50 avg rating — 2 ratings
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| 5 |
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Concentration Inequalities: A Nonasymptotic Theory of Independence
by
4.56 avg rating — 18 ratings
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| 6 |
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Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics, Series Number 25)
by
4.38 avg rating — 13 ratings
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| 7 |
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Information Geometry (Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge / A Series of Modern Surveys in Mathematics) (v. 64)
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really liked it 4.00 avg rating — 1 rating
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| 8 |
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High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48)
by
4.68 avg rating — 25 ratings
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| 8 |
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Foundations of Data Science
by
4.24 avg rating — 25 ratings
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| 10 |
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High-Dimensional Probability: An Introduction with Applications in Data Science
by
4.68 avg rating — 34 ratings
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| 10 |
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Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis
by
4.50 avg rating — 2 ratings
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| 12 |
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Machine Learning: A Bayesian and Optimization Perspective
by
4.19 avg rating — 16 ratings
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| 12 |
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Foundations of Machine Learning
by
4.21 avg rating — 94 ratings
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| 14 |
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Kernel Methods for Pattern Analysis
by
3.96 avg rating — 28 ratings
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| 15 |
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Bandit Algorithms
by
4.57 avg rating — 14 ratings
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| 15 |
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Handbook of Practical Logic and Automated Reasoning
by
3.85 avg rating — 13 ratings
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| 17 |
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Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3)
by
4.29 avg rating — 21 ratings
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| 17 |
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An Introduction to Description Logic
by
4.50 avg rating — 10 ratings
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| 19 |
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
by
4.05 avg rating — 40 ratings
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| 19 |
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Algorithms for Reinforcement Learning
by
4.04 avg rating — 26 ratings
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| 21 |
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The Nature of Statistical Learning Theory
by
4.26 avg rating — 34 ratings
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| 21 |
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Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics)
by
4.47 avg rating — 17 ratings
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| 23 |
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)
by
4.17 avg rating — 108 ratings
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| 23 |
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Fuzzy Set Theory―and Its Applications
by
4.30 avg rating — 10 ratings
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| 25 |
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Dynamic Programming And Optimal Control, Vol. 1
by
4.36 avg rating — 33 ratings
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| 25 |
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Dynamic Programming And Optimal Control, Vol. 1
by
4.36 avg rating — 33 ratings
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| 27 |
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Dynamic Programming and Optimal Control
by
3.96 avg rating — 27 ratings
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| 27 |
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Dynamic Programming and Optimal Control, Vol. 2
by
4.19 avg rating — 21 ratings
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| 29 |
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Dynamic Programming and Optimal Control, Vol. 2
by
4.19 avg rating — 21 ratings
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| 29 |
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The Nature of Statistical Learning Theory
by
4.26 avg rating — 34 ratings
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| 31 |
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Introduction to Stochastic Dynamic Programming
by
really liked it 4.00 avg rating — 7 ratings
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| 31 |
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The Minimum Description Length Principle
by
4.08 avg rating — 12 ratings
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| 33 |
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A Primer on Reproducing Kernel Hilbert Spaces
by
liked it 3.00 avg rating — 2 ratings
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| 33 |
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Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 44)
by
4.33 avg rating — 3 ratings
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| 35 |
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An Introduction to the Theory of Reproducing Kernel Hilbert Spaces (Cambridge Studies in Advanced Mathematics, Series Number 152)
by
really liked it 4.00 avg rating — 3 ratings
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| 35 |
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Data Science for Mathematicians
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really liked it 4.00 avg rating — 1 rating
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| 37 |
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Reproducing Kernel Hilbert Spaces in Probability and Statistics
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really liked it 4.00 avg rating — 3 ratings
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| 37 |
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Principal Component Analysis (Springer Series in Statistics)
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4.25 avg rating — 16 ratings
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| 39 |
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Unsupervised Learning: Foundations of Neural Computation
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really liked it 4.00 avg rating — 18 ratings
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| 39 |
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Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)
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4.50 avg rating — 4 ratings
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| 41 |
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Nonlinear Programming
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4.43 avg rating — 37 ratings
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| 42 |
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Reinforcement Learning: An Introduction
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4.54 avg rating — 797 ratings
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| 43 |
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Algorithms for Reinforcement Learning
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4.04 avg rating — 26 ratings
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From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (Foundations and Trends
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4.50 avg rating — 2 ratings
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| 45 |
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Metric Learning: A Survey
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0.00 avg rating — 0 ratings
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| 46 |
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Bayesian Reinforcement Learning: A Survey
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did not like it 1.00 avg rating — 1 rating
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| 47 |
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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
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3.68 avg rating — 22 ratings
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| 48 |
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All of Nonparametric Statistics
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4.15 avg rating — 40 ratings
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| 49 |
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Introduction to Nonparametric Estimation (Springer Series in Statistics)
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4.22 avg rating — 9 ratings
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| 50 |
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Convex Optimization
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4.48 avg rating — 345 ratings
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| 51 |
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Deep Learning
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4.44 avg rating — 2,083 ratings
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| 52 |
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Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability, No. 57)
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3.43 avg rating — 7 ratings
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| 53 |
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Simulation
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| 54 |
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Foundations of Data Science
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