Master MATLAB through Guided Problem Solving – Tim Buchalka and Mike X Cohen
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Master MATLAB through Guided Problem Solving – Tim Buchalka and Mike X Cohen
WHAT WILL YOU LEARN?
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Intermediate to advanced MATLAB programming proficiency. This is *the only course* that develops intermediate and advanced programming skills.
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Obtain real-world application experience that that researchers and industry professionals use MATLAB for, including signal processing, matrix decompositions, spectral analysis, linear and nonlinear model-fitting, and statistics.
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Gain skills to solve challenging problems in MATLAB, as opposed to memorizing syntax rules.
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Data science projects and problem-solving in MATLAB
REQUIREMENT
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A computer with MATLAB or Octave installed.
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Many people have access to MATLAB through their school, university, or company.
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MATLAB also offers student licenses and free 30-day trials.
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Octave is a free cross-platform scientific computing environment that has nearly identical functionality as MATLAB.
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Some prior expose to MATLAB programming is highly recommended.
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This course starts from the basics but builds quickly to intermediate/advanced programming levels.
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Previous experience with other programming languages can be beneficial but is not necessary.
DESCRIPTION
MATLAB is the premier programming language of scientific computing in university, research, and industry.
Excellent MATLAB programming skills can make or break a career in engineering, physics, biology, finance, and many other fields.
But how do you learn how to program in MATLAB? Sure, you can watch some tutorial videos online, but those videos generally focus on the most basic coding skills, like what is a variable and how to write a for-loop.
Those are important foundations, but they won’t make you competitive. If you want to be competitive, you need to become a master at MATLAB programming. And that’s what you’ll learn in this course.
This course is different from other MATLAB programming courses!
Rather than having short videos that presents a simple piece of MATLAB syntax, this course starts from the real-world perspective: Problems in data science that you can solve with MATLAB.
Do you need to learn how to filter your signal in MATLAB? There’s a section for that!
Do you have a complicated formula and you need to translate it into MATLAB code and make a graph that simulates the formula with different parameters? Yeah, there’s a section on that as well!
Do you need to make a movie of a 3D simulation changing in color over time and then export as an .avi file? This course covers that as well!
Maybe you want to learn how to make textured landscapes using fractal math, or use SVD to compute the low-rank approximation of a data matrix, or use nonlinear search methods to fit a 3-parameter Gaussian model to data? You guessed it — it’s all covered in this course!
If you are looking for a slow, gentle course that will take baby-steps to teach you what you could have learned on your own, then this is not that course.
If you want to gain confidence in solving real-world problems in MATLAB coding to advance your studies, your research, and your career by learning MATLAB programming from a single course with >35 hours of hands-on instruction from a world-class scientist, teacher, and writer, then this course is definitely right for you.
Key topics covered.
The basic stuff: loops, functions, IO, etc. Core skills you need to be able to progress with MATLAB for scientific computing or data science.
The more intermediate and advanced topics in the course include:
2D and 3D plotting, and data animations (movies)
Image and time series segmentation
Calculus and differential equations
Univariate and multivariate time series cleaning and analysis
Spectral (Fourier) and time-frequency analyses
Linear algebra including eigendecomposition, orthogonalization, and principal components analysis
Generating fractal pictures and time series
Nonparametric statistics and nonlinear model fitting
Get started today!
There is no doubt that MATLAB is the premier programming language of scientific computing and data science in university, research, and industry.
The sooner you sign up for this course, the sooner you will be able to advance your career in engineering, physics, biology, finance (or other fields using MATLAB).
Why not get started today?
Click the Signup button to sign up for the course!
WHO IS THIS FOR?
- Anyone looking to build a career in engineering, physics, biology, finance, and many other fields by building Excellent MATLAB coding skills
- Anyone wanting to progress beyond watching simple MATLAB tutorials to actually becoming an expert in MATLAB programming by solving real problems in scientific computing
CURRICULUM
Course Introduction
4 sessions • 21 min
- flmportantl How to Learn from this Course v Aperçu 11:20
- Stages of Learning Programming and Completing Projects v 05:11
Pdf with complete lecture list and keywords 00:02
- Using the Q&A forum 04:47
Getting Started 5 sessions • 55 min
- Create, edit, and open scripts v 16:35
- Write Comments in Lines and Blocks v 09:52
- Using MATLAB for a Personal Budget v 16:22
- Personalize MATLAB Colors v 06:40
- Start MATLAB with an Encouraging Note v 05:14
Vectors and Variables
- Create Vectors and Matrices v O Unsolved: Valid and Invalid Matrices v O Working with Text (Characters and Strings) v O HTML Table from MATLAB Code v O Round pi to N significant Digits v O File/folder Information Using Structures v
Command Statements
- Create a Hilbert Matrix Using for-loops. v O Unsolved: Compare two while Loops v O Create an Upper-Trianaular Matrix v O Random Count-Down Timer fPoisson-liket v
- Display the day of 1 January
Import and Export Data
- Save and Load Multiple Files v O Import Formatted Text Data v O Import Excel-format Data v O Convert USS to Euros Using up-to-date Info
Translate Formulas into Code
- Trig Functions and Gaussians v O Laplace and log-normal Distributions
- Complex Numbers and Euler’s Formula
- Piecewise Functions
- Piecewise Function in one line of Code
- Sigmoid Function
- Unsolved: Sigmoid and Error Function v O Circular p-value and Its Approximation
Descriptive Statistics
- Compute Measures of Central Tendency v O Compute Variance and Standard Deviation O Unsolved: Sort Data up and down v
- Data Transformations (log, sqrt, rank)
2D Plotting
- Lines v
- Bars and errorbars v
- Dots v
- Multidimensional Data with Colored Scatter v
- Unsolved: imagesc vs. pcolor v
- Histograms v
- Uncertainty in Future Money (using patch) v
- Blend Pictures Using Transparency v
- Vertically Stacking Data Series v O Distance Matrix from Generated Points v
- Gabor Patch Marginal Histograms v D List of other image-processing videos in this course v
3D Plotting
- Sphere in a Cube
- Colorful Cube (a.k.a. the happy Borg ship)
- Expanding Wavelets with Surfaces
- Textured Gaussian Surfaces
- A Ball in 3D Color Space v O Plane in R3 Spanned by Two Vectors v Complex Sine Surface
- The Prickly Gabor Patch
Segmentation
- Threshold-based Time Series Segmentation
- Derivative-based Time Series Segmentation
- Intensity-based Image Segmentation
- Identify Neurons in a Mouse Brain Slice
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