Books - Revision history

Vs at 21:52, 12 February 2024

2024-02-12T21:52:04Z

Wiki at 17:25, 19 March 2023

2023-03-19T17:25:45Z

Wiki at 21:12, 20 February 2023

2023-02-20T21:12:56Z

Wiki at 21:07, 20 February 2023

2023-02-20T21:07:56Z

Wiki at 19:46, 20 February 2023

2023-02-20T19:46:29Z

Wiki at 19:45, 20 February 2023

2023-02-20T19:45:24Z

Wiki at 19:42, 20 February 2023

2023-02-20T19:42:38Z

Wiki at 19:39, 20 February 2023

2023-02-20T19:39:20Z

Wiki: /* Russian edition */

2023-02-20T19:28:51Z

Russian edition

Wiki: /* Functional data analysis */

2022-11-14T15:55:18Z

Functional data analysis

← Older revision		Revision as of 21:52, 12 February 2024
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		+	{{#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]]		Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees, [[Educational program]] and [[Media:Ds program 2023.png\|poster]]

← Older revision		Revision as of 17:25, 19 March 2023
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−	~~[[Media:Ds program 2023.png\|~~Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees]]	+	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.		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.

← Older revision		Revision as of 21:12, 20 February 2023
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	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.		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==		==Machine learning for beginners==

← Older revision		Revision as of 21:07, 20 February 2023
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	[[Media:Ds program 2023.png\|Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees]]		[[Media:Ds program 2023.png\|Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees]]
		+
		+	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==		==Machine learning for beginners==

← Older revision		Revision as of 19:46, 20 February 2023
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−	[[Media:~~Ds_program_2023~~\|Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees]]	+	[[Media:Ds program 2023.png\|Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees]]

	==Machine learning for beginners==		==Machine learning for beginners==

← Older revision		Revision as of 19:45, 20 February 2023
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−	[[Media:~~ds_program_2023~~\|Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees]]	+	[[Media:Ds_program_2023\|Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees]]

	==Machine learning for beginners==		==Machine learning for beginners==

← Older revision		Revision as of 19:42, 20 February 2023
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−	[[Media:~~DS2023program.pdf~~\|Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees]]	+	[[Media:ds_program_2023\|Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees]]

	==Machine learning for beginners==		==Machine learning for beginners==

← Older revision		Revision as of 19:39, 20 February 2023
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		+	[[Media:DS2023program.pdf\|Data Science: A Roadmap for Bachelor, Master, and Doctoral Degrees]]
		+
	==Machine learning for beginners==		==Machine learning for beginners==
	* [https://arxiv.org/pdf/1709.02840 A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018]		* [https://arxiv.org/pdf/1709.02840 A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone, 2017-2018]

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 * [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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 ==Functional data analysis==
 * Sequences and series in banach spaces by J. Diestel, 1984
 * Functional data analysis by J.O. Ramsay and B.W. Silverman, 2005