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Clogs
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Machine Learning for the Pure Mathematician
(34 Books)
1
A Probabilistic Theory of Pattern Recognition
by
Luc Devroye
3.92 avg rating — 13 ratings
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2
Foundations of Machine Learning
by
Mehryar Mohri
4.21 avg rating — 94 ratings
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3
Understanding Machine Learning
by
Shai Shalev-Shwartz
4.20 avg rating — 132 ratings
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4
High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48)
by
Martin J. Wainwright
4.67 avg rating — 24 ratings
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5
High-Dimensional Probability: An Introduction with Applications in Data Science
by
Roman Vershynin
4.68 avg rating — 34 ratings
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6
Machine Learning: A Bayesian and Optimization Perspective
by
Sergios Theodoridis
4.24 avg rating — 17 ratings
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7
Concentration Inequalities: A Nonasymptotic Theory of Independence
by
Stéphane Boucheron
4.56 avg rating — 18 ratings
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8
Bandit Algorithms
by
Tor Lattimore
4.62 avg rating — 16 ratings
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9
Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3)
by
Dimitri P. Bertsekas
4.29 avg rating — 21 ratings
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10
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
by
Bernhard Schölkopf
4.05 avg rating — 40 ratings
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11
The Nature of Statistical Learning Theory
by
Vladimir N. Vapnik
4.29 avg rating — 35 ratings
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12
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)
by
Carl Edward Rasmussen
4.17 avg rating — 109 ratings
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13
Dynamic Programming And Optimal Control, Vol. 1
by
Dimitri P. Bertsekas
4.36 avg rating — 33 ratings
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14
Dynamic Programming and Optimal Control
by
Dimitri P. Bertsekas
3.96 avg rating — 27 ratings
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15
Dynamic Programming and Optimal Control, Vol. 2
by
Dimitri P. Bertsekas
4.19 avg rating — 21 ratings
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16
Introduction to Stochastic Dynamic Programming
by
Sheldon M. Ross
really liked it
4.00 avg rating — 8 ratings
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17
A Primer on Reproducing Kernel Hilbert Spaces
by
Jonathan H. Manton
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3.00 avg rating — 2 ratings
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18
An Introduction to the Theory of Reproducing Kernel Hilbert Spaces (Cambridge Studies in Advanced Mathematics, Series Number 152)
by
Vern I. Paulsen
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4.00 avg rating — 3 ratings
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19
Reproducing Kernel Hilbert Spaces in Probability and Statistics
by
Alain Berlinet
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4.00 avg rating — 3 ratings
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20
Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)
by
Ovidiu Calin
4.60 avg rating — 5 ratings
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21
Nonlinear Programming
by
Dimitri P. Bertsekas
4.43 avg rating — 37 ratings
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22
Reinforcement Learning: An Introduction
by
Richard S. Sutton
4.54 avg rating — 816 ratings
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23
Algorithms for Reinforcement Learning
by
Csaba Szepesvari
4.04 avg rating — 27 ratings
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24
From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (Foundations and Trends
by
Remi Munos
4.50 avg rating — 2 ratings
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25
Metric Learning: A Survey
by
Brian Kulis
0.00 avg rating — 0 ratings
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26
Bayesian Reinforcement Learning: A Survey
by
Mohammad Ghavamzadeh
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1.00 avg rating — 1 rating
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27
Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
by
Laura Graesser
3.68 avg rating — 22 ratings
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28
All of Nonparametric Statistics
by
Larry Wasserman
4.13 avg rating — 39 ratings
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29
Introduction to Nonparametric Estimation (Springer Series in Statistics)
by
Alexandre B. Tsybakov
4.20 avg rating — 10 ratings
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30
Convex Optimization
by
Stephen Boyd
4.48 avg rating — 349 ratings
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31
Deep Learning
by
Ian Goodfellow
4.44 avg rating — 2,112 ratings
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32
Stochastic Simulation: Algorithms and Analysis
by
Søren Asmussen
3.43 avg rating — 7 ratings
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33
Simulation
by
Sheldon M. Ross
4.08 avg rating — 48 ratings
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34
Foundations of Data Science
by
Avrim Blum
4.24 avg rating — 25 ratings
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