Mastering Linear Algebra: An Introduction with Applications

Take your math skills to a new level with linear algebra, which is used in everything from computer graphics to quantum mechanics.
Mastering Linear Algebra: An Introduction with Applications is rated 4.5 out of 5 by 50.
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Rated 5 out of 5 by from Extension to other math topics very well done Long ago I took a course in linear algebra and enjoyed it. Later I used parts of it in quantum mechanics, so eigenstuff was fairly familiar. But the big picture -- how linear algebra can deal with systems of equations in many surprising contexts -- was very well presented here. I gained perspective from this course, and I congratulate Prof. Su for his masterful explanations!
Date published: 2021-06-14
Rated 5 out of 5 by from I finally get it I never understood linear algebra and its relevance to my work (digital marketing) until now. In particular, I never got Eigenvectors...now I do as the Professor made the material approachable, going at the right pace and completeness. This is not easy subject matter, but he handles it exceptionally well
Date published: 2021-01-11
Rated 5 out of 5 by from Understanding Vectors and Matrices ! The course gives one a very good understanding of eigenvectors and matrices. I would recommend this course to people that love mathematics.
Date published: 2020-11-16
Rated 4 out of 5 by from Fasten Your Seatbelt and Stay Alert To begin, both the course description and Dr. Su play down the necessary background in math to be successful in taking this course, saying that some calculus would be helpful, but not necessary. While that may be technically true, both the pace that the course delivers information and concepts, and the assumption by Professor Su as to the level of knowledge of his students is of a fairly high level. For example he frequently uses terms, without explanation, in discussing concepts that require a familiarity with math at a reasonable level. It is hard for me to imagine that most persons, if not all, who are taking this course will have had at least a course or two of calculus. At the least in TTC terms, I would suggest the trig and pre-calc class as well as one of the calculus ones. Perhaps it is only me, but after the first lecture, I went back and reviewed matrix math, as I felt that I was really not prepared for the speed at which the information was delivered, as well as the assumed knowledge of these skills by the professor. Not that this is a bad thing. I often feel that many of the courses offered by TTC are a little too basic and/or do not assume enough background of those taking the courses and therefore spend far too much time in delivering elementary material. There are exceptions to this general statement, the course on thermodynamics and a couple of the philosophy courses are examples requiring considerable background. Dr. Su has a low-key style, only occasionally giving us a bit of humor to leaven the material. He is soft, but well spoken, being precise, but not spending any time explaining things he thinks that we should know. This is all to the good. Where he and the course fall down is trying to cover too much material in too short a time. At least for me, I would have benefited more from the same material over 36 lectures instead of 24. This would allow for a few more examples and a bit more explanation. OTOH, for those who skills are not so ancient as mine, the pace is likely right. In any case I applaud TTC for presenting this course. I am aware that the target audience is pretty limited, but it is a gem for those of us who love math. Recommended if you are one, but if you are not, give it a pass.
Date published: 2020-11-05
Rated 5 out of 5 by from Helps understand the meaning - I've needed this I am glad I found this. Finally Linear Algebra is making some sense! I'm really only near the beginning, not close to done, but I am already thrilled that I am getting some meaning out of linear algebra instead of just performing algorithms and memorizing terms and definitions. I am doing self study and if you are too, you will also need a regular book or course with lots of exercises, because this course focuses on meaning and understanding. I should add, I have been looking at multiple sources both online and in books regarding linear algebra, and it may be that it is all of the sources together that is finally making linear algebra make sense. For anyone hesitating, I should point out the reason I am doing this is because every time I try to self study a "more interesting" higher level math subject, the math seems to rely on knowing linear algebra well first. I kept putting it off because linear algebra seemed relatively boring. The reason it was boring is because I was not really getting the meaning, not getting a deep understanding of what it all meant. I'm going for it now and am glad I did and this course really helps. I should note - they ask in the review what level of prior knowledge I had before this course and they only have 3 options. I would say I am more than a beginner but not by much - not quite intermediate. I would also like to point out I am reading the book as much as I am watching the video. I like the book because I can read my favorite parts over easily and mark pages. I like having both.
Date published: 2020-09-12
Rated 5 out of 5 by from Excellent Course! This course was a recent purchase for me. I preceded it with the Understanding Calculus I & II Great Courses. One could take the video lectures on its own and would benefit from it. On the other hand I wanted to gain a solid understanding of the Course. Just like any university course, I did the homework provided with the course. It was very beneficial and the problems really helped. I purchased the text book "Linear Algebra and its Applications, 5th Edition". It was a great supplement to the guide book and provided additional exercises. To further apply my knowledge I used a Wolfram Alpha subscription to reproduce examples in the lectures, as well as an aid in the home work. Just like college, instruction, textbook, homework and lab leads to a full understanding.
Date published: 2020-09-02
Rated 4 out of 5 by from Thought provoking Introduction. The good..... The basics of Linear Algebra presented are very good. The presented applications are very good. The overall course is a great refresher and stirs ones curiosity. The bad.... This is really only an Introduction (Hardly Masting LA). As an introduction it has far too few lectures and insufficient exercises/details. On the whole, informative and enjoyable.
Date published: 2020-06-25
Rated 4 out of 5 by from Basics & Beyond... I have a BS in Applied Mathematics and always wanted a better understanding of Linear Algebra. I particularly liked the treatment of Eigenvalues. This course had a good balance of concepts and mechanics. I sometimes wished that Prof. Su would do an example and then develop the theory. Overall a good course if you have some mathematics background.
Date published: 2020-06-01
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Overview

Taught by Professor Francis Su of Harvey Mudd College, this course covers the topics of a first-semester college course in linear algebra, including vector spaces, dot and cross products, matrix operations, linear transformations, determinants, eigenvectors and eigenvalues, and much more. Professor Su introduces many fascinating applications of linear algebra, from computer graphics to quantum mechanics.

About

Francis Su
Francis Su

Linear algebra is about seeing the world visually in a completely different way. It’s about growing in your ability to recognize the hidden mathematical structures that underlie the everyday problems we encounter.

INSTITUTION

Harvey Mudd College

Francis Su is the Benediktsson-Karwa Professor of Mathematics at Harvey Mudd College. He earned his Ph.D. from Harvard University, and he has held visiting professorships at Cornell University and the Mathematical Sciences Research Institute in Berkeley, California. In 2015 and 2016, he served as president of the Mathematical Association of America (MAA).

 

Professor Su’s research focuses on geometric and topological combinatorics and their applications to the social sciences. He has published numerous scientific papers, and his work on the rental harmony problem (the question of how to divide rent fairly among roommates) was featured in The New York Times. He also wrote the book, Mathematics and Human Flourishing.

 

Professor Su’s teaching and writing are nationally renowned. The MAA has recognized his work with the Deborah and Franklin Tepper Haimo Award and the Henry L. Alder Award for exemplary teaching, as well as the Paul R. Halmos-Lester R. Ford Award and the Merten M. Hasse Prize for distinguished writing. He is the author of the popular Math Fun Facts website; has a widely used YouTube course on real analysis; and is the creator the math news app MathFeed,. Professor Su’s notoriety as a popularizer prompted WIRED magazine to call him, “the mathematician who will make you fall in love with numbers.”

By This Professor

Mastering Linear Algebra: An Introduction with Applications
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Mastering Linear Algebra: An Introduction with Applications

Trailer

Linear Algebra: Powerful Transformations

01: Linear Algebra: Powerful Transformations

Discover that linear algebra is a powerful tool that combines the insights of geometry and algebra. Focus on its central idea of linear transformations, which are functions that are algebraically very simple and that change a space geometrically in modest ways, such as taking parallel lines to parallel lines. Survey the diverse linear phenomena that can be analyzed this way.

28 min
Vectors: Describing Space and Motion

02: Vectors: Describing Space and Motion

Professor Su poses a handwriting recognition problem as an introduction to vectors, the basic objects of study in linear algebra. Learn how to define a vector, as well as how to add and multiply them, both algebraically and geometrically. Also see vectors as more general objects that apply to a wide range of situations that may not, at first, look like arrows or ordered collections of real numbers.

27 min
Linear Geometry: Dots and Crosses

03: Linear Geometry: Dots and Crosses

Even at this stage of the course, the concepts you’ve encountered give insight into the strange behavior of matter in the quantum realm. Get a glimpse of this connection by learning two standard operations on vectors: dot products and cross products. The dot product of two vectors is a scalar, with magnitude only. The cross product of two vectors is a vector, with both magnitude and direction.

28 min
Matrix Operations

04: Matrix Operations

Use the problem of creating an error-correcting computer code to explore the versatile language of matrix operations. A matrix is a rectangular array of numbers whose rows and columns can be thought of as vectors. Learn matrix notation and the rules for matrix arithmetic. Then see how these concepts help you determine if a digital signal has been corrupted and, if so, how to fix it.

31 min
Linear Transformations

05: Linear Transformations

Dig deeper into linear transformations to find out how they are closely tied to matrix multiplication. Computer graphics is a perfect example of the use of linear transformations. Define a linear transformation and study properties that follow from this definition, especially as they relate to matrices. Close by exploring advanced computer graphic techniques for dealing with perspective in images.

28 min
Systems of Linear Equations

06: Systems of Linear Equations

One powerful application of linear algebra is for solving systems of linear equations, which arise in many different disciplines. One example: balancing chemical equations. Study the general features of any system of linear equations, then focus on the Gaussian elimination method of solution, named after the German mathematician Carl Friedrich Gauss, but also discovered in ancient China.

28 min
Reduced Row Echelon Form

07: Reduced Row Echelon Form

Consider how signals from four GPS satellites can be used to calculate a phone’s location, given the positions of the satellites and the times for the four signals to reach the phone. In the process, discover a systematic way to use row operations to put a matrix into reduced row echelon form, a special form that lets you solve any system of linear equations, and tells you a lot about the solutions.

28 min
Span and Linear Dependence

08: Span and Linear Dependence

Determine whether eggs and oatmeal alone can satisfy goals for obtaining three types of nutrients. Learn about the span of a set of vectors, which is the set of all linear combination of those vectors; and linear dependence, where one vector can be written as a linear combination of two others. Along the way, develop your intuition for seeing possible solutions to problems in linear algebra.

31 min
Subspaces: Special Subsets to Look For

09: Subspaces: Special Subsets to Look For

Delve into special subspaces of a matrix: the null space, row space, and column space. Use these to understand the economics of making croissants and donuts for a specified price, drawing on three ingredients with changing costs. As in the previous lecture, move back and forth between a matrix equation, a system of equations, and a vector equation, which all represent the same thing.

29 min
Bases: Basic Building Blocks

10: Bases: Basic Building Blocks

Using the example of digital compression of images, explore the basis of a vector space. This is a subset of vectors that, in the case of compression formats like JPEG, preserve crucial information while dispensing with extraneous data. Discover how to find a basis for a column space, row space, and null space. Also make geometric observations about these important structures.

29 min
Invertible Matrices: Undoing What You Did

11: Invertible Matrices: Undoing What You Did

Now turn to engineering, a fertile field for linear algebra. Put yourself in the shoes of a bridge designer, faced with determining the maximum force that a bridge can take for a given deflection vector. This involves the inverse of a matrix. Explore techniques for determining if an inverse matrix exists and then calculating it. Also learn proofs about properties of matrices and their inverses.

30 min
The Invertible Matrix Theorem

12: The Invertible Matrix Theorem

Use linear algebra to analyze one of the games on the popular electronic toy Merlin from the 1970s. This leads you deeper into the nature of the inverse of a matrix, showing why invertibility is such an important idea. Learn about the fundamental theorem of invertible matrices, which provides a key to understanding properties you can infer from matrices that either have or don’t have an inverse.

34 min
Determinants: Numbers That Say a Lot

13: Determinants: Numbers That Say a Lot

Study the determinant—the factor by which a region of space increases or decreases after a matrix transformation. If the determinant is negative, then the space has been mirror-reversed. Probe other properties of the determinant, including its use in multivariable calculus for computing the volume of a parallelepiped, which is a three-dimensional figure whose faces are parallelograms.

30 min
Eigenstuff: Revealing Hidden Structure

14: Eigenstuff: Revealing Hidden Structure

Dive into eigenvectors, which are a special class of vectors that don’t change direction under a given linear transformation. The scaling factor of an eigenvector is the eigenvalue. These seemingly incidental properties turn out to be of enormous importance in linear algebra. Get started with “eigenstuff” by pondering a problem in population modeling, featuring foxes and their prey, rabbits.

27 min
Eigenvectors and Eigenvalues: Geometry

15: Eigenvectors and Eigenvalues: Geometry

Continue your study from the previous lecture by exploring the geometric properties of eigenvectors and eigenvalues, gaining an intuitive sense of the hidden structure they reveal. Learn how to calculate eigenvalues and eigenvectors; and for vectors that are not eigenvectors, discover that if you have a basis of eigenvectors, then it’s easy to see how a transformation moves every other point.

29 min
Diagonalizability

16: Diagonalizability

In this third lecture on eigenvectors, examine conditions under which a change in basis results in a basis of eigenvectors, which makes computation with matrices very easy. Discover the property called diagonalizability, and prove that being diagonalizable is the equivalent to having a basis of eigenvectors. Also explore the connection between the eigenvalues of a matrix and its determinant.

32 min
Population Dynamics: Foxes and Rabbits

17: Population Dynamics: Foxes and Rabbits

Return to the problem of modeling the population dynamics of foxes and rabbits from Lecture 14, drawing on your knowledge of eigenvectors to analyze different scenarios. First, express the predation relationship in matrix notation. Then, experiment with different values for the predation factor, looking for the optimum ratio of foxes to rabbits to ensure that both populations remain stable.

30 min
Differential Equations: New Applications

18: Differential Equations: New Applications

Professor Su walks you through the application of matrices in differential equations, assuming for just this lecture that you know a little calculus. The first problem involves the population ratios of rats and mice. Next, investigate the motion of a spring, using linear algebra to simplify second order differential equations into first order differential equations—a handy simplification.

33 min
Orthogonality: Squaring Things Up

19: Orthogonality: Squaring Things Up

In mathematics, “orthogonal” means at right angles. Difficult operations become simpler when orthogonal vectors are involved. Learn how to determine if a matrix is orthogonal and survey the properties that result. Among these, an orthogonal transformation preserves dot products and also angles and lengths. Also, study the Gram–Schmidt process for producing orthogonal vectors.

32 min
Markov Chains: Hopping Around

20: Markov Chains: Hopping Around

The algorithm for the Google search engine is based on viewing websurfing as a Markov chain. So are speech-recognition programs, models for predicting genetic drift, and many other data structures. Investigate this practical tool, which employs probabilistic rules to advance from one state to the next. Find that Markov chains converge on at least one steady-state vector, an eigenvector.

33 min
Multivariable Calculus: Derivative Matrix

21: Multivariable Calculus: Derivative Matrix

Discover that linear algebra plays a key role in multivariable calculus, also called vector calculus. For those new to calculus, Professor Su covers essential concepts. Then, he shows how multivariable functions can be translated into linear transformations, which you have been studying since the beginning. See how other ideas in multivariable calculus also fall into place, thanks to linear algebra.

31 min
Multilinear Regression: Least Squares

22: Multilinear Regression: Least Squares

Witness the wizardry of linear algebra for finding a best-fitting line or best-fitting linear model for data—a problem that arises whenever information is being analyzed. The methods include multiple linear regression and least squares approximation, and can also be used to reverse-engineer an unknown formula that has been applied to data, such as U.S. News and World Report’s college rankings.

28 min
Singular Value Decomposition: So Cool

23: Singular Value Decomposition: So Cool

Next time you respond to a movie, music, or other online recommendation, think of the singular value decomposition (SVD), which is a matrix factorization method used to match your known preferences to similar products. Learn how SVD works, how to compute it, and how its ability to identify relevant attributes makes it an effective data compression tool for subtracting unimportant information.

32 min
General Vector Spaces: More to Explore

24: General Vector Spaces: More to Explore

Finish the course by seeing how linear algebra applies more generally than just to vectors in the real coordinate space of n dimensions, which is what you have studied so far. Discover that Fibonacci sequences, with their many applications, can be treated as vector spaces, as can Fourier series, used in waveform analysis. Truly, linear algebra pops up in the most unexpected places!

34 min