Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
10 Hour(s) 2 Minute(s)
Language
English
Taught by
Ahmed Fathy, MSc
Rating
Course Overview
Linear Algebra (matlab - python) & Matrix Calculus For Machine Learning, Robotics, Computer Graphics, Control, & more !
From Matrix Calculus, To Robotics! From Control Systems, To Computer Graphics! From the Singular Value Decompositions to the Principal Component Analysis. From Systems Of Linear Equations, To Systems Of Differential Equations. From Inverses, to Pseudo Inverses. From Determinants, to positive definiteness. From Concepts To Programming. From Matlab To Python. From Proofs to Visualizations & From Theory to Applications. From Solved Examples To thoughtful Exams, and From Many Other Things to Many other things,
I, Present This Course !
My Name is Ahmed Fathy, currently a machine learning scientist at Affectiva, and a university teacher previously. Over the years, I happened to teach many subjects that make a very deep use of linear algebra. Those include Machine Learning and Deep Learning, Computer Graphics, Control Systems, Game Development, and even Pure Linear Algebra. Every one of those subjects handled linear algebra from very different perspectives. In this course, I provide them all.
This course is intended to be a Reference on linear algebra in the world of online courses, having proofs, theories, programming, concepts, applications, solved examples, visualizations, and everything ! Any suggestions for more topics to add are always welcome. Since the course contents are so large and extensive, I will not summarize them here. Instead, I ask you to please watch the promo video & also have a look on the course contents towards the bottom of the page. Have a nice day !
Course Content
- 36 section(s)
- 296 lecture(s)
- Section 1 Introduction To The Course
- Section 2 Introduction To Matrices : Linear Independence And Matrix Multiplication
- Section 3 Introduction To Gaussian Elimination And Matrix Inverse
- Section 4 Test Your Self ! - Exam 1 !
- Section 5 The Computer Graphics Section !
- Section 6 The Robotics Section !
- Section 7 Test Your Self ! - Exam 2 !
- Section 8 Test Your Self ! - Exam 3 !
- Section 9 EigenValues & EigenVectors ( I ) : Introduction
- Section 10 EigenValues & EigenVectors ( II ) : Difference Equations
- Section 11 EigenValues & EigenVectors ( III ) : Differential Equations
- Section 12 Test Your Self ! - Exam 4 !
- Section 13 Matrix Inverse Using Cofactors & The Cayley Hamilton Theorem
- Section 14 Back To Systems Of Linear Equations ! - The Matrix Rank
- Section 15 The Four Sub-spaces Of A Matrix
- Section 16 Solving The Unsolvable : Linear Regression, Projection Matrix & Normal Equation
- Section 17 Test Your Self ! - Exam 5 !
- Section 18 A Section On Symmetric Matrices
- Section 19 A Section On Machine Learning And DataScience
- Section 20 Appendix A - The Lagrange Multipliers
- Section 21 The Principal Component Analysis (PCA)
- Section 22 Test Your Self ! - Exam 6 !
- Section 23 The Singular Value Decomposition (SVD)
- Section 24 The Pseudo Inverse Of A Matrix
- Section 25 Test Your Self ! - Exam 7 !
- Section 26 The LU Decomposition
- Section 27 A Video On Positive Definite Matrices - Will Come Back In A Subsequent Section !
- Section 28 Appendix B : The Taylor Expansion
- Section 29 Back To Positive Definite Matrices !
- Section 30 Determinants !
- Section 31 Matrix Calculus - I : The Basics
- Section 32 Matrix Calculus II : On The Relation Between The Jacobian And Double Integrals
- Section 33 Matrix Calculus - III : More On Matrix Calculus
- Section 34 Test Your Self ! - The Final Exam !
- Section 35 EXTRA - I :: Homogeneous Coordinates And The Projection Matrix Derivation !
- Section 36 BONUS : Get My Other Courses !
What You’ll Learn
- Gain Deep Understanding Of Linear Algebra Theoretically, Conceptually & Practically., Obtain A Very Robust Mathematical Foundation For Machine & Deep Learning, Computer Graphics, And Control Systems., Learn How To Use Both Python And Matlab For Solving & Visualizing Linear Algebra Problems., [Matrix Calculus] Learn How To Differentiate & Optimize Complex Equations Involving Matrices., Learn A Lot About Data Science, Co-variance Matrices, And The PCA., Learn About Linear Regression, The Normal Equation, And The Projection Matrix., Learn About Singular Value Decompositions Formally & Conceptually., Learn About Inverses And Pseudo Inverses., Learn About Determinants And Positive Definite Matrices., Learn How To Solve Systems Of Linear, Difference, & Differential Equations Both By Hand And Software., Learn About Lagrange Multipliers & Taylor Expansion., Learn About The Hessian Matrix And Its Importance In Multi-variable Calculus & Optimizations., Learn About Complex Transformation Matrices Like The Matrix To Perform Rotation Around An Arbitrary Axis In 3D., And Much More ! This is a 34+ hours course !
Skills covered in this course
Reviews
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AAnonymized User
so much details, very good explanation and bite size course "decomposition"
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TTR M
Excellent Coverage. I found the course extremely useful.
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VVagner
I expected that there would be a computational application of linear algebra in this course, as advertised, but it is almost nonexistent. I was really disappointed with this course. I came for the names Python and Matlab, but there is almost nothing.
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MMojtaba Kolahdouzi
Very useful course! It provided me with a deeper insight into matrices and matrix calculus! I think every student studying deep learning should watch this course! Just please be patient to complete the course since it is a long course!
