New Educational Methods for Learning Mathematics
https://www.youtube.com/watch?v=X_CK1e0Lmxw
#mathematics
https://www.youtube.com/watch?v=X_CK1e0Lmxw
#mathematics
YouTube
Common Core Math Explained
Dr. Raj Shah, owner and founder of Math Plus Academy (mathplusacademy.com) explains why math is taught differently than it was in the past and helps address parents' misconceptions about the "new math". Original video: http://vimeo.com/110807219
A fascinating Youtube Channel for learning various mathematical concepts:
https://www.youtube.com/channel/UCFe6jenM1Bc54qtBsIJGRZQ
#mathematics
https://www.youtube.com/channel/UCFe6jenM1Bc54qtBsIJGRZQ
#mathematics
YouTube
Patrick J
Howdy!
I've been creating free Mathematics videos since 2007 and continue to do so.
Teachers please feel free to reach out if I can help you although I do get a lot of emails! You can find my email below next to the 'For Business Inquiries' box!
If you…
I've been creating free Mathematics videos since 2007 and continue to do so.
Teachers please feel free to reach out if I can help you although I do get a lot of emails! You can find my email below next to the 'For Business Inquiries' box!
If you…
Lectures Slides of Signal Processing for Machine Learning Course by Stanfrod University
http://web.stanford.edu/class/ee269/slides.html
#mathematics #machine_learning
http://web.stanford.edu/class/ee269/slides.html
#mathematics #machine_learning
A great course about Digital Signal Processing, presented by EPFL
https://www.coursera.org/learn/dsp
#mathematics #electrical_engineering
https://www.coursera.org/learn/dsp
#mathematics #electrical_engineering
Coursera
Digital Signal Processing 1: Basic Concepts and Algorithms
Offered by École Polytechnique Fédérale de Lausanne. ... Enroll for free.
The Math of Machine Learning - Berkeley University Textbook
The mathematical skills you need for starting your journey into the field of Machine Learning
Note: It should be noted that It doesn't cover all the mathematical skills you need for doing ML during your life, It's just a brief textbook which could help you to start learning more complicated mathematical concepts in ML
https://www.datasciencecentral.com/profiles/blogs/tutorial-the-math-of-machine-learning-berkeley-university
#mahine_learning #mathematics
The mathematical skills you need for starting your journey into the field of Machine Learning
Note: It should be noted that It doesn't cover all the mathematical skills you need for doing ML during your life, It's just a brief textbook which could help you to start learning more complicated mathematical concepts in ML
https://www.datasciencecentral.com/profiles/blogs/tutorial-the-math-of-machine-learning-berkeley-university
#mahine_learning #mathematics
Data Science Central
The Math of Machine Learning - Berkeley University Textbook - DataScienceCentral.com
This document is an attempt to provide a summary of the mathematical background needed for an introductory class in machine learning, which at UC Berkeley is known as CS 189/289A. Our assumption is that the reader is already familiar with the basic concepts…
Necessity of complex numbers in Quantum Mechanics
https://www.youtube.com/watch?v=f079K1f2WQk
#mathematics #quantum_physics
https://www.youtube.com/watch?v=f079K1f2WQk
#mathematics #quantum_physics
YouTube
Necessity of complex numbers
MIT 8.04 Quantum Physics I, Spring 2016
View the complete course: http://ocw.mit.edu/8-04S16
Instructor: Barton Zwiebach
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
View the complete course: http://ocw.mit.edu/8-04S16
Instructor: Barton Zwiebach
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Mathematics for Machine Learning
Summary: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
https://mml-book.github.io/book/mml-book.pdf
#machine_learning #mathematics
Summary: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
https://mml-book.github.io/book/mml-book.pdf
#machine_learning #mathematics