Difference between revisions of "Books"

Latest revision as of 00:52, 13 February 2024

Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees, Educational program and poster

Education in Machine learning has changed drastically in recent years. The main roadstones were deep learning, reinforcement learning, and now it is physics-informed or geometric deep learning. The requirements for mathematical knowledge rise, even for engineering parts. An example is differential programming techniques. Below we present bachelor and master programs for modern Machine learning. We call it Knowledge-aware machine learning.

Machine learning for beginners

Linear algebra

Optimization

Basics of probability and statistics

A first course in probability by Sheldon M. Ross, 2012
Elements of information theory by Thomas M. Cover, Joy A. Thomas, 2006
Probability theory by Alexandr A. Borovkov, 2006
Mathematical statistics by Alexandr A. Borovkov, 1999
Linear Statistical Inference & Its Applications by C. Radhakrishna Rao, 1967
Linear Models and Generalizations: Least Squares and Alternatives by C. Radhakrishna Rao et al., 2007

Bayesian statistics and inference

Bayesian reasoning and machine learning by David Barber, 2014
Probabilistic graphical models by Daphne Koller and Nir Friedman, 2009
Machine learning: a probabilistic perspective by Kevin P. Murphy, 2012
Bayesian data analysis by Andrew Gelman et al., 2013

Functional data analysis

Functional Analysis by Peter D. Lax, 2002.
Sequences and series in banach spaces by J. Diestel, 1984
Functional data analysis by J.O. Ramsay and B.W. Silverman, 2005
Solving Differential Equations on Manifolds by Ernst Hairer, 2011

Discrete analysis

Lectures on discrete geometry by Jiří Matoušek, 2002
Indiscrete thoughts by Gian-Carlo Rota, 2008
Graph theory by Reinhard Diestel, 2017
Graph theory (groups and symmetries: from finite groups to Lie groups) by Reinhard Diestel, 2000

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-=Machine learning for beginners=
+{{#seo:
+ |title=Books on Machine Learning
+ |titlemode=replace
+ |keywords=Books on Machine Learning
+ |description=Education in Machine learning has changed drastically in recent years. The main roadstones were deep learning, reinforcement learning, and now it is physics-informed or geometric deep learning.
+ }}
+Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees, [[Educational program]] and [[Media:Ds program 2023.png|poster]]
+Education in Machine learning has changed drastically in recent years. The main roadstones were deep learning, reinforcement learning, and now it is physics-informed or geometric deep learning. The requirements for mathematical knowledge rise, even for engineering parts. An example is differential programming techniques. Below we present bachelor and master programs for modern Machine learning. We call it Knowledge-aware machine learning.
+<!--
+==Mathematics==
+Discrete analysis and graphs
+Abstract algebra and group theory
+Mathematical and functional analysis
+ODE, PDE, and mathematical modeling
+Measure and Probability
+Linear algebra
+Tensor algebra and calculus
+Theoretical physics
+Differential geometry
+Scientific computation and numerical methods
+Multivariate statistics
+Bayesian statistics and Graphical models
+Stochastic processes and SDE
+Bayesian model selection
+Computer science courses
+Programming
+Software architectures
+System analysis
+Category theory
+Parallel and distributed computing
+==Optimization and Control==
+Discrete optimization
+Convex optimization
+Mathematical programming
+==Core Data Science ==
+Machine learning and data analysis
+Deep learning
+Generative models
+Reinforcement and online learning
+Geometric deep learning
+==Applied Data Science==
+Signal analysis
+Computer vision
+Audio processing
+Topic modeling and Information retrieval
+Recommendation systems
+Multimedia and heterogeneous data
+Bioinformatics
+Brain-computer interfaces and metaverse
+*** essential, ** recommended, * advanced
+-->
+==Machine learning for beginners==
 * [https://arxiv.org/pdf/1709.02840 A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018]
 * [https://www.semanticscholar.org/paper/Understanding-Machine-Learning%3A-From-Theory-to-Shalev-Shwartz-Ben-David/ce615ae61d67db8537e981a0a08da7f0f2ff1cee Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Shai Ben-David, 2014]
@@ Line 8: / Line 72: @@
 * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie Robert Tibshirani Jerome Friedman, 2008]
-=Linear algebra=
+==Linear algebra==
 *[https://drive.google.com/file/d/16SL9bQYar2ylDzHKNapBIDXCYUZEnRIh/view?fbclid=IwAR3fiuQgDJ0PRxv8o6UslbGx2ICdKxO2Li32FtwPJ_GbjRCXKhxa-BPZw2A Linear algebra by Jörg Liesen, Volker Mehrmann, 2015]
 *[http://joshua.smcvt.edu/linearalgebra Linear algebra by Jim Hefferon, 2017]
 *[https://web.stanford.edu/~boyd/vmls/?fbclid=IwAR08VCHfJ1hVAvuVBW6G59CZZ9EWzAlm0yKnID82DP9G2YbmugzsYIQQ4W0 Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe, 2018]
-=Optimization=
+==Optimization==
 * [https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf Convex Optimization by Stephen Boyd and Lieven Vandenberghe, 2009]
 * [https://archive.siam.org/books/textbooks/fr18_book.pdf Iterative Methods for Optimization by C.T.Kelley, 1999]
-=Basic statistics=
+==Basics of probability and statistics==
-* Probability Theory Alexandr A. Borovkov, 2006
+* A first course in probability by Sheldon M. Ross, 2012
-* Mathematical statistics by A.A. Borovkov, 1999
+* Elements of information theory by Thomas M. Cover, Joy A. Thomas, 2006
+* Probability theory by Alexandr A. Borovkov, 2006
+* Mathematical statistics by Alexandr A. Borovkov, 1999
 * Linear Statistical Inference & Its Applications by C. Radhakrishna Rao, 1967
 * Linear Models and Generalizations: Least Squares and Alternatives by C. Radhakrishna Rao et al., 2007
-=Bayesian statistics and inference=
+==Bayesian statistics and inference==
 * [http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf Bayesian reasoning and machine learning by David Barber, 2014]
 * Probabilistic graphical models by Daphne Koller and Nir Friedman, 2009
@@ Line 29: / Line 95: @@
 * Bayesian data analysis by Andrew Gelman et al., 2013
-=Functional data analysis=
+==Functional data analysis==
+* Functional Analysis by Peter D. Lax, 2002.
+* Sequences and series in banach spaces by J. Diestel, 1984
 * Functional data analysis by J.O. Ramsay and B.W. Silverman, 2005
 * [https://www.unige.ch/~hairer/poly-sde-mani.pdf Solving Differential Equations on Manifolds by Ernst Hairer, 2011]
-=Programming=
+==Discrete analysis==
+* Lectures on discrete geometry by Jiří Matoušek, 2002
+* Indiscrete thoughts by Gian-Carlo Rota, 2008
+* Graph theory by Reinhard Diestel, 2017
+* Graph theory (groups and symmetries: from finite groups to Lie groups) by Reinhard Diestel, 2000
+==Programming==
 * [https://unglueit-files.s3.amazonaws.com/ebf/617027d14a3046998f54b31503ab7bca.pdf Python notes for professionals by GoalKicker.com Free Programming Books, 2020]
 * [https://d2l.ai/ Dive into Deep Learning by  Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, 2020]
-=Russian edition=
+==Ru==
 * [http://www.1variant.ru/content/uchebniki/matematika/650.pdf Лагутин М.Б. Наглядная математическая статистика, 2009] (cм. также [http://files.lbz.ru/pdf/cC2125-4-ch.pdf вырезку])
 * [https://www.artlebedev.ru/izdal/spravochnik-izdatelya-i-avtora/ Аркадий Мильчин и Людмила Чельцова. Справочник издателя и автора (Редакционно-издательское оформление издания), 2018]

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