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Three Month Plan to Learn Mathematics Behind Machine Learning

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In this article, I have shared a 3-month plan to learn mathematics for machine learning. As we know, almost all machine learning algorithms make use of concepts of Linear Algebra, Calculus, Probability & Statistics, etc. Some advanced algorithms and techniques also make use of subjects such as Measure Theory(a superset of probability theory), convex and non-convex optimization, and much more. To understand the machine learning algorithms and conduct research in machine learning and its related fields, the knowledge of mathematics becomes a requirement. The plan that I have shared in this article can be used to prepare for data science interviews, to strengthen mathematical concepts, or to start researching in machine learning. The plan will not only help in understanding the intuition behind machine learning but can also be used in many other advanced fields such as statistical signal processing, computational electrodynamics, etc.


Artificial intelligence expert originates new theory for decision-making

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That's the question faced by Prakash Shenoy, the Ronald G. Harper Distinguished Professor of Artificial Intelligence at the University of Kansas School of Business. His answer can be found in the article "An Interval-Valued Utility Theory for Decision Making with Dempster-Shafer Belief Functions," which appears in the September issue of the International Journal of Approximate Reasoning. "People assume that you can always attach probabilities to uncertain events," Shenoy said. "But in real life, you never know what the probabilities are. You don't know if it's 50 percent or 60 percent. This is the essence of the theory of belief functions that Arthur Dempster and Glenn Shafer formulated in the 1970s."


Probability Theory in Data Science

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The 4 Most Common Probability Distributions Used in Data Science. Probability distributions are one of the most used concepts of maths that are used in various real-life applications. From weather prediction to the stock market to machine learning applications, different probability distributions are the basic building blocks of all these applications and more. Probability distributions are one of the most used concepts of maths that are used in various real-life applications. From weather prediction to the stock market to machine learning applications, different probability distributions are the basic building blocks of all these applications and more.


The Data Science Course 2020 Q2 Updated: Part 1

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New Created by Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight! English [Auto]00 Students also bought The Data Science Course 2020 Q2 Updated: Part 3 Docker for Beginners Data Structure & Algorithms using C: Zero To Mastery 2020 Python for Data Science and Machine Learning beginners Geospatial Data Analyses & Remote Sensing: 4 Classes in 1 Preview this course GET COUPON CODE Description "Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician." More often than not participants rush into learning data science without knowing what exactly they are getting into: this course will give you insights and clarity on what data science is all about. Statistics, Math, Linear Algebra If we talk in general about Data Science, then for a serious understanding and work we need a fundamental course in probability theory (and therefore, mathematical analysis as a necessary tool in probability theory), linear algebra and, of course, mathematical statistics. Fundamental mathematical knowledge is important in order to be able to analyze the results of applying data processing algorithms. There are examples of relatively strong engineers in machine learning without such a background, but this is rather the exception.


Learning AI/ML: The Hard Way - DZone AI

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Data science, Artificial Intelligence (AI) and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner's hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.



The abstraction of probability theory.

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This discussion on'The Abstractionism of probability" is perhaps one of the first in the world to be discussed publicly. It has to be understood that, this discussion has evolved out of various other discussions with mathematicians, philosophers, doctors and engineers and many other participants including rappers, mainstream musicians, artists, actors and actresses. Because this was the subject matter for a documentary, to keep its serenity and purity, no filmmakers of any kind were interviewed. The film is in the making.


16. Appendix: Mathematics for Deep Learning -- Dive into Deep Learning 0.7 documentation

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One of the wonderful parts of modern deep learning is the fact that much of it can be understood and used without a full understanding of the mathematics below it. This is a sign of the fact that the field is becoming more mature. Most software developers no longer need to worry about the theory of computable functions, or if programming languages without a goto can emulate programming languages with a goto with at most constant overhead, and neither should the deep learning practitioner need to worry about the theoretical foundations maximum likelihood learning, if one can find an architecture to approximate a target function to an arbitrary degree of accuracy. That said, we are not quite there yet. Sometimes when building a model in practice you will need to understand how architectural choices influence gradient flow, or what assumptions you are making by training with a certain loss function.


16. Appendix: Mathematics for Deep Learning -- Dive into Deep Learning 0.7 documentation

#artificialintelligence

One of the wonderful parts of modern deep learning is the fact that much of it can be understood and used without a full understanding of the mathematics below it. This is a sign of the fact that the field is becoming more mature. Most software developers no longer need to worry about the theory of computable functions, or if programming languages without a goto can emulate programming languages with a goto with at most constant overhead, and neither should the deep learning practitioner need to worry about the theoretical foundations maximum likelihood learning, if one can find an architecture to approximate a target function to an arbitrary degree of accuracy. That said, we are not quite there yet. Sometimes when building a model in practice you will need to understand how architectural choices influence gradient flow, or what assumptions you are making by training with a certain loss function.


A Formal Proof of PAC Learnability for Decision Stumps

arXiv.org Machine Learning

We present a machine-checked, formal proof of PAC learnability of the concept class of decision stumps. A formal proof has every step checked and justified using fundamental axioms of mathematics. We construct and check our proof using the Lean theorem prover. Though such a proof appears simple, a few analytic and measure-theoretic subtleties arise when carrying it out fully formally. We explain how we can cleanly separate out the parts that deal with these subtleties by using Lean features and a category theoretic construction called the Giry monad.