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.
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.
Rated 3 out of
5
by
Nice Goat from
Decent course but room for improvement
Needs more visuals and less droning about variables into the camera. Also, the “rich-middle class-poor” Markov chain is incorrect. Should be [0.05063, 0.56962, 0.37975].
Date published: 2023-12-20
Rated 5 out of
5
by
Doug Blanding from
Mastering Linear Algebra
This course is Fantastic!
I studied engineering before the age of computers. When I was in engineering school, the lunar mission trajectories were computed using slide rules. Linear algebra was not something I needed to learn. Eventually, this ended up being a big gap in my technical education. When I got curious to understand how computer graphics works or how robot navigation works, I was totally lost. I have no clue about matrix multiplication or eigenvectors. It was all mumbo-jumbo to me.
So this course was perfect for me. Dr. Su does a great job of explaining the obscure (up until now) aspects of linear algebra, bringing it all easily within my reach. He introduces each new concept with a relevant, but simple example, showing the student in detail exactly how to complete each step of the process.
Date published: 2023-11-26
Rated 5 out of
5
by
BobK41 from
Wonderfully taught
Dr. Su is a wonderful teacher: clear and concise. Having just completed this course, I offer the following suggestions. Musts are completing mathematic courses of geometry and algebra II. Also, a must is having a linear algebra textbook to use. I checked mine out of the library. Recommended are having taken a calculus course, primarily for the last several lessons.
Date published: 2023-07-29
Rated 5 out of
5
by
Andrew421 from
Outstanding
This is an amazing course. A detailed subject is presented in a an accessible way by a superb lecturer. As well as describing the key principles of the subject there are lots of interesting examples of how linear algebra is used. I highly recommend it.
Date published: 2023-02-25
Rated 3 out of
5
by
Charlie6024 from
Needs 36 lectures. Turn off the screen savers.
Get the books, and read and study them till, you understand them in their principal features....
November 5, 1855 letter from Abraham Lincoln to Isham Reavis.
I realize that linear algebra was still evolving in the first half of the 19th century, but I don’t know how useful Lincoln’s advice is for linear algebra.
During the past 4 years, I have worked my way through three semesters of calculus with Professor Edwards as my lecturer and through differential equations with the help of someone I found on-line. I assume that the people with very natural mathematical abilities will flow very naturally and easily through Professor Su’s Mastering Linear Algebra: An Introduction with Applications. Those of us who could not do 2 weeks of high school trigonometry/pre calculus in one night - yes, I had such a high school friend - will probably be a lot more challenged.
The course guidebook states, “This course will follow David Lay, Steven Lay, and Judi McDonald’s Linear Algebra and Its Applications and David Poole’s Linear Algebra: A Modern Introduction (both listed in the Bibliography), but almost any text in linear algebra will do if you look at the sections covering the topics indicated.” See page 4. Professor Poole’s “To the Instructor” section (3rd edition) outlines the sections that he recommends for a one semester course. Professor Su mixes the sections on matrix operations into the lectures on eigenvalues and eigenvectors, but seemingly he did not have enough lectures to focus solely on matrices first. For example, the suggested readings for Lecture 3 direct students to Poole sections 1.2 and 1.3 and for Lecture 4 skip to Poole section 3.7. Lectures 6 through 13 address the subjects in Poole’s chapter 2, and the matrix topics of Poole chapter 3 (Matrices) are mixed (mashed or crammed?) into the lectures for the topics of Poole chapter 4.
Math is not quite natural enough to me to build the foundation while I’m working on the second or third floor. I do not know whether Professor Su or The Great Courses chose a 24-lecture format for Mastering Linear Algebra, but for me there was too much material crammed into too few lectures.
During this course, I have learned a lot about how I learn math. Among other things, I need to see examples of the procedure(s) to solve the exercises at the end of each section of a text. If I can figure out the details for each step, I can work my way through the exercises. The level of detail in Professor Edwards’ calculus courses was enough. Professor Su’s 24 lectures did not have enough time for the same amount of detail.
Finally, from one camera angle we see two television screens in the background. Each plays a constantly moving geometric screen saver. However, just as the chariot scene of Ben-Hur playing in the background of Professor Aldrete’s Rise of Rome course might be remotely connected to the subject, the screen savers are an unnecessary distraction from linear algebra.
Date published: 2022-02-27
Rated 5 out of
5
by
Mark J from
Wish I'd had this while in grad school
If I'd had this course when I was working on my Masters (Mathematics) my graduate GPA would have been significantly higher. This course is a far better explanation of linear algebra, and its relationship to other areas of mathematics, than anything I ever took in a classroom.
Date published: 2022-02-07
Rated 5 out of
5
by
Hotel Whiskey from
An excellent introduction.
I had my first encounter with Linear Algebra back in 1974 when I was boning-up aboard the USS Mount Whitney (LCC-20), preparing to begin graduate study in the field of control systems engineering. It was a subject that I had frequent call for in the next thirty-five years of a career in the pulp, paper, chemical, automotive and aerospace industries. I think Prof. Su’s treatment is first rate. A delightful refresher. The lectures are well organized and complemented with excellent graphics to illustrate the geometric interpretation of linear transformations. I especially liked lectures #19 (Orthoganality: Squaring Things Up), #23 (Singular Value Decomposition: So Cool) and #24 (General Vector Spaces: More to Explore). HWF, Mesa AZ.
Date published: 2021-11-24
Rated 3 out of
5
by
James62 from
Good lectures, not so good guidebook
I enjoyed the course but could have gotten much more if the lectures included more examples with all the steps shown (show your work, teacher!). The guidebook was not very useful. The course could be improved to excellent if the guidebook had about 10 worked examples for each chapter, and also showed the steps in the too-few examples used in the lectures. I would rate the lectures 4.5 and the guidebook 2.
Date published: 2021-09-30
Overview
About
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.”
Trailer
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
