Machine Learning for the Pure Mathematician

1.
A Probabilistic Theory of Pattern Recognition
A Probabilistic Theory of Pattern Recognition 3.92 avg rating — 13 ratings
2.
Foundations of Machine Learning
Foundations of Machine Learning 4.21 avg rating — 94 ratings
3.
Understanding Machine Learning
Understanding Machine Learning 4.21 avg rating — 131 ratings
3.
Geometric Modeling in Probability and Statistics
Geometric Modeling in Probability and Statistics 4.50 avg rating — 2 ratings
5.
Concentration Inequalities: A Nonasymptotic Theory of Independence
Concentration Inequalities: A Nonasymptotic Theory of Independence 4.56 avg rating — 18 ratings
6.
Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics, Series Number 25)
Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics, Series Number 25) 4.38 avg rating — 13 ratings
7.
Information Geometry (Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge / A Series of Modern Surveys in Mathematics) (v. 64)
Information Geometry (Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge / A Series of Modern Surveys in Mathematics) (v. 64)
by: Nihat Ay
really liked it 4.00 avg rating — 1 rating
8.
High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48)
High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48) 4.68 avg rating — 25 ratings
8.
Foundations of Data Science
Foundations of Data Science 4.24 avg rating — 25 ratings
10.
High-Dimensional Probability: An Introduction with Applications in Data Science
High-Dimensional Probability: An Introduction with Applications in Data Science 4.67 avg rating — 33 ratings
10.
Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis
Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis 4.50 avg rating — 2 ratings
12.
Machine Learning: A Bayesian and Optimization Perspective
Machine Learning: A Bayesian and Optimization Perspective 4.19 avg rating — 16 ratings
12.
Foundations of Machine Learning
Foundations of Machine Learning 4.21 avg rating — 94 ratings
14.
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis 3.96 avg rating — 28 ratings
15.
Bandit Algorithms
Bandit Algorithms 4.57 avg rating — 14 ratings
15.
Handbook of Practical Logic and Automated Reasoning
Handbook of Practical Logic and Automated Reasoning 3.85 avg rating — 13 ratings
17.
Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3)
Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3) 4.29 avg rating — 21 ratings
17.
An Introduction to Description Logic
An Introduction to Description Logic 4.50 avg rating — 10 ratings
19.
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 4.05 avg rating — 40 ratings
19.
Algorithms for Reinforcement Learning
Algorithms for Reinforcement Learning 4.04 avg rating — 26 ratings
21.
The Nature of Statistical Learning Theory
The Nature of Statistical Learning Theory 4.24 avg rating — 33 ratings
21.
Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics)
Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics) 4.47 avg rating — 17 ratings
23.
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) 4.17 avg rating — 108 ratings
23.
Fuzzy Set Theory―and Its Applications
Fuzzy Set Theory―and Its Applications 4.30 avg rating — 10 ratings
25.
Dynamic Programming And Optimal Control, Vol. 1
Dynamic Programming And Optimal Control, Vol. 1 4.36 avg rating — 33 ratings
25.
Dynamic Programming And Optimal Control, Vol. 1
Dynamic Programming And Optimal Control, Vol. 1 4.36 avg rating — 33 ratings
27.
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control 3.88 avg rating — 26 ratings
27.
Dynamic Programming and Optimal Control, Vol. 2
Dynamic Programming and Optimal Control, Vol. 2 4.19 avg rating — 21 ratings
29.
Dynamic Programming and Optimal Control, Vol. 2
Dynamic Programming and Optimal Control, Vol. 2 4.19 avg rating — 21 ratings
29.
The Nature of Statistical Learning Theory
The Nature of Statistical Learning Theory 4.24 avg rating — 33 ratings
31.
Introduction to Stochastic Dynamic Programming
Introduction to Stochastic Dynamic Programming really liked it 4.00 avg rating — 7 ratings
31.
The Minimum Description Length Principle
The Minimum Description Length Principle 4.08 avg rating — 12 ratings
33.
A Primer on Reproducing Kernel Hilbert Spaces (Foundations and Trends
A Primer on Reproducing Kernel Hilbert Spaces (Foundations and Trends liked it 3.00 avg rating — 2 ratings
33.
Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 44)
Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 44) 4.33 avg rating — 3 ratings
35.
An Introduction to the Theory of Reproducing Kernel Hilbert Spaces (Cambridge Studies in Advanced Mathematics, Series Number 152)
An Introduction to the Theory of Reproducing Kernel Hilbert Spaces (Cambridge Studies in Advanced Mathematics, Series Number 152) really liked it 4.00 avg rating — 3 ratings
35.
Data Science for Mathematicians
Data Science for Mathematicians really liked it 4.00 avg rating — 1 rating
37.
Reproducing Kernel Hilbert Spaces in Probability and Statistics
Reproducing Kernel Hilbert Spaces in Probability and Statistics really liked it 4.00 avg rating — 3 ratings
37.
Principal Component Analysis (Springer Series in Statistics)
Principal Component Analysis (Springer Series in Statistics) 4.25 avg rating — 16 ratings
39.
Unsupervised Learning: Foundations of Neural Computation
Unsupervised Learning: Foundations of Neural Computation 3.94 avg rating — 17 ratings
39.
Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)
Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences) 4.50 avg rating — 4 ratings
41.
Nonlinear Programming
Nonlinear Programming 4.43 avg rating — 37 ratings
42.
Reinforcement Learning: An Introduction
Reinforcement Learning: An Introduction 4.54 avg rating — 793 ratings
43.
Algorithms for Reinforcement Learning
Algorithms for Reinforcement Learning 4.04 avg rating — 26 ratings
44.
From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (Foundations and Trends
From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (Foundations and Trends 4.50 avg rating — 2 ratings
45.
Metric Learning: A Survey (Foundations and Trends
Metric Learning: A Survey (Foundations and Trends 0.00 avg rating — 0 ratings
46.
Bayesian Reinforcement Learning: A Survey (Foundations and Trends
Bayesian Reinforcement Learning: A Survey (Foundations and Trends did not like it 1.00 avg rating — 1 rating
47.
Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) 3.68 avg rating — 22 ratings
48.
All of Nonparametric Statistics
All of Nonparametric Statistics 4.15 avg rating — 40 ratings
49.
Introduction to Nonparametric Estimation (Springer Series in Statistics)
Introduction to Nonparametric Estimation (Springer Series in Statistics) 4.22 avg rating — 9 ratings
50.
Convex Optimization
Convex Optimization 4.48 avg rating — 343 ratings
51.
Deep Learning
Deep Learning 4.44 avg rating — 2,067 ratings
52.
Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability, No. 57)
Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability, No. 57) 3.43 avg rating — 7 ratings
53.
Simulation
Simulation 4.06 avg rating — 47 ratings
54.
Foundations of Data Science
Foundations of Data Science 4.24 avg rating — 25 ratings