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This is an advanced course which discusses about the iterative methods to solve matrix system of equations. This course will teach you how the order of millions or even billions of states and how all the bits of information can be tracked to solve the system.

Disease modeling can be solved by using this method to recognize the health state for every human on earth with more advanced scientific computing skills after finishing this course.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

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Course Credit: University of Washington

Course Curriculum

Lecture1 : Vectors & Matrices 00:42:00
Lecture2 : Logic, Loops, and Iterations 00:39:00
Lecture3 : Plotting & Importing/Exporting Data 00:45:00
Lecture4 : Supplement: About your computer 00:08:00
Lecture5 : Supplement: A bit more about your computer 00:08:00
Lecture6 : Supplement: Benchmarking 00:10:00
Lecture7 : Supplement: Computational Complexity 00:12:00
Lecture8 : Supplement: Element-wise multiplication 00:07:00
Lecture9 : Supplement: Dot-times 00:10:00
Lecture10 : Supplement: Function Handles 00:07:00
Lecture11 : Linear Systems of Equations 00:42:00
Lecture12 : Supplement: Matrix Modeling 00:09:00
Lecture13 : Supplement: Condition Numbers 00:07:00
Lecture14 : Supplement: More on condition numbers 00:13:00
Lecture15 : Gaussian Elimination for Ax=b 00:40:00
Lecture16 : LU Matrix Decomposition for Ax=b 00:51:00
Lecture17 : Supplement: LU decomposition 00:09:00
Lecture18 : Iteration Methods for Ax-b 00:47:00
Lecture19 : Eigenvalues and Eigenvectors 00:44:00
Lecture20 : Eigen-decompositions and Iterations 00:49:00
Lecture21 : The Singular Value Decomposition (SVD) 00:45:00
Lecture22 : Principal Componenet Analysis (PCA) 00:51:00
Lecture23 : PCA for Face Recognition 00:48:00
Lecture24 : Least-Squares Fitting Methods 00:45:00
Lecture25 : Polynomial Fits and Splines 00:44:00
Lecture26 : Data Fitting with Matlab 00:39:00
Lecture27 : Unconstrained Optimization (Derivative-Free Methods) 00:46:00
Lecture28 : Unconstrained Optimization (Derivative Methods) 00:49:00
Lecture29 : Linear Programming and Genetic Algorithms 00:57:00
Lecture30 : Numerical Differentiation Methods 00:47:00
Lecture31 : Supplement: Mean Value Theorem 00:06:00
Lecture32 : Higher-order Accuracy Schemes for Differentiation and Integration 00:45:00
Lecture33 : Higher-order Integration Schemes 00:50:00
Lecture34 : Ordinary Differential Equations and Time-stepping 00:47:00
Lecture35 : Error and Stability of Time-stepping Schemes 00:45:00
Lecture36 : General Time-stepping and Runge-Kutta Schemes 00:45:00
Lecture37 : Supplement: Using ODE45 & Runge-Kutta methods 00:09:00
Lecture38 : Application of Runge-Kutta to Lorenz Equation 00:30:00
Lecture39 : Supplement: Vector fields and phase-planes 00:06:00
Lecture40 : Vectorized Time-step Integrators 00:41:00
Lecture41 : Supplement: Indexing equations 00:10:00
Lecture42 : Supplement: Big systems of ODEs 00:05:00
Lecture43 : Application of Runge-Kutta to Chaotic Dynamics and the Double Pendulum 00:50:00
Lecture44 : Theory of the Fourier Transform 00:49:00
Lecture45 : Discrete Fourier Transform (DFT) and the Fast Fourier Transform (FFT) 00:48:00
Lecture46 : Supplement: Discrete Fourier Transform 00:08:00
Lecture47: FFT and Image Compression 00:43:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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