Hi, I am currently preparing to apply to the OMSA program later this year. I know that the OMSA program has a linear algebra requirement, but I am struggling to find a reputable source for where I can take linear algebra online, and for credit. Do you have any recommendations?
Machine learning algorithms typically require knowledge of scalar, vectors, and matrices to compute loss functions, eigenvalues, covariance matrix, etc. Further, linear algebra is also used extensively in neural networks, regularization techniques, recommender systems, Singular Value Decomposition (SVD), Principal Component Analysis (PCA), etc.
Linear Algebra Course
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It is a playlist of 16 videos that include concepts such as cross products, dot products, eigenvectors, eigenvalues, etc. 3Blue1Brown is a youtube channel that focuses on teaching mathematical concepts in an easy-to-understand manner using unique visualization techniques. Though it does not qualify as a course per se, the channel has made its way to our list of recommendations because of its unique theme of inventing and visualizing math.
It is a good course to learn the fundamentals of linear algebra like the vector product, linear transformation, finding determinants, etc. If you are looking to quickly revisit the basics, then refer to this link.
It is a short course with a 10-video playlist that focuses on why you need to learn linear algebra for machine learning. The course is a primer to understand linear algebra concepts well within 90 minutes.
Khan Academy and 3Blue1Brown videos are easy to understand and help you pick up the pace as a complete beginner. Once you have learned the concepts from these resources, you are set to learn the deeper and more comprehensive material from the courses suggested below.
Learners who do not have sufficient python background can also get started with this course, as it guides them through short code blocks with focused concepts. It is spread over a period of four weeks and requires 19 hours to complete.
The post has listed five popular courses to master linear algebra. The best part is that all the listed courses are free of cost and ranked from beginner-level to more advanced concepts. While the ML community keeps questioning if it is essential to learn linear algebra to get started with machine learning, I would highly recommend following a more agile approach to keep iterating and referring to these courses as you chart out your ML algorithmic journey.
Vidhi Chugh is an award-winning AI/ML innovation leader and an AI Ethicist. She works at the intersection of data science, product, and research to deliver business value and insights. She is an advocate for data-centric science and a leading expert in data governance with a vision to build trustworthy AI solutions.
This is an introductory online math course with an emphasis on computation rather than proof. For the purposes of this online course, the computations will be done by hand, which is a necessary first step in understanding how they are done.
You have 3 to 9 months from your enrollment date to complete 12 lessons and 3 proctored exams using ProctorU Live+. The lessons consist of readings from the text and notes by the instructor, a set of suggested exercises from the text which are for practice and not for submission, and a problem set which is to be submitted as homework. The first two exams cover material in the preceding lessons, while the final exam covers all material presented in the course.
A typical first linear algebra course focuses on how to solve matrix problems by hand, for instance, spending time using Gaussian Elimination with pencil and paper to solve a small system of equations manually. However, it turns out that the methods and concerns for solving larger matrix problems via a computer are often drastically different:
This course uses the same top down, code first, application centered teaching method as we used in our Practical Deep Learning for Coders course, and which I describe in more detail in this blog post and this talk I gave at the San Francisco Machine Learning meetup. Basically, our goal is to get students working on interesting applications as quickly as possible, and then to dig into the underlying details later as time goes on.
The primary resource for this course is the free online textbook of Jupyter Notebooks, available on Github. They are full of explanations, code samples, pictures, interesting links, and exercises for you to try. Anyone can view the notebooks online by clicking on the links in the readme Table of Contents. However, to really learn the material, you need to interactively run the code, which requires installing Anaconda on your computer (or an equivalent set up of the Python scientific libraries) and you will need to be able to clone or download the git repo.
I should note: I am interested in the Computational Linear Algebra course as well. I think I probably need to feel more comfortable with the mechanics. Furthermore, my number one goal in learning Linear Algebra at the moment is understanding papers and other mathy deep learning material. Perhaps others can comment on how the CLA course fits into that path.
It depends on your performance in the course. If you are able to handle the accelerated pace and maintain a good understanding of the material, it should not negatively impact your GPA. However, if you struggle with the pace and do not perform well, it could potentially lower your GPA.
It ultimately depends on your individual learning style and abilities. Some students may thrive in a fast-paced environment, while others may struggle to keep up. It is important to consider your own strengths and weaknesses before deciding to take an accelerated course.
It may be possible to switch to the regular course, but it will depend on the policies of your institution and the availability of spots in the regular course. It is important to consider your decision carefully before enrolling in an accelerated course.
I saw that you mentioned =PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab as the pre requisite. I was planning to do this course on OCW -06-linear-algebra-spring-2010/video-lectures/ to fill my gaps in linear algebra. If you are aware of this course would this fulfill the pre requisites?
Hi, According to the syllabus there is also mention of linear and robust regression, so is basic knowledge of probability and statistics needed? or just the linear algebra prerequisites mentioned in this section -linear-algebra/blob/master/nbs/0.%20Course%20Logistics.ipynb#Linear-Algebra
Multivariable Calculus and Linear Algebra provides unified coverage of linear algebra and multivariable differential calculus, and is based on recently developed curriculum by the Stanford University Mathematics faculty for first-year Stanford University students. The course e-text, written by a group of Stanford University Mathematics faculty, connects the material to many fields. Linear algebra in large dimensions underlies the scientific, data-driven, and computational tasks of the 21st century. The course emphasizes computations alongside an intuitive understanding of key ideas. The widespread use of computers makes it important for users of math to understand concepts: innovative users of quantitative tools in the future will be those who understand ideas and how they fit with examples and applications. This course is different than many multivariable calculus and linear algebra courses offered in high schools and colleges in that it is more abstract and challenging without being proof-based.
Orthogonality, linear independence, matrix algebra, and eigenvalues with applications such as least squares, linear regression, and Markov chains (relevant to population dynamics, molecular chemistry, and PageRank); the singular value decomposition (essential in image compression, topic modeling, and data-intensive work in many fields) is introduced in the final chapter of the e-text.
Can anyone suggest a relatively gentle linear algebra text that integrates vector spaces and matrix algebra right from the start? I've found in the past that students react in very negative ways to the introduction of abstract vector spaces mid-way through a course. Sometimes it feels as though I've walked into class and said "Forget math. Let's learn ancient Greek instead." Sometimes the students realize that Greek is interesting too, but it can take a lot of convincing! Hence I would really like to let students know, right from the start, what they're getting themselves into.
For teaching the type of course that Dan described, I'd like to recommend David Lay's "Linear algebra". It is very thoroughly thought out and well written, with uniform difficulty level, some applications, and several possible routes/courses that he explains in the instructor's edition. Vector spaces are introduced in Chapter 4, following the chapters on linear systems, matrices, and determinants. Due to built-in redundancy, you can get there earlier, but I don't see any advantage to that. The chapter on matrices has a couple of sections that "preview" abstract linear algebra by studying the subspaces of $\mathbb{R}^n$.
I rather like Linear Algebra Done Right, and depending on the type of students you are aiming the course for, I would recommend it over Hoffman and Kunze. Since you seemed worried that Axler might be too advanced, my feeling is that Hoffman and Kunze will definitely be (especially if these are students who have never been taught proof-based mathematics).
Hands-down, my favorite text is Hoffman and Kunze's Linear Algebra. Chapter 1 is a review of matrices. From then on, everything is integrated. The abstract definition of a vector space is introduced in chapter 2 with a review of field theory. Chapter 3 is all about abstract linear transformations as well as the representation of such transformations as matrices. I'm not going to recount all of the chapters for you, but it seems to be exactly what you want. It's also very flexible for teaching a course. It includes sections on modules and derives the determinant both classically and using the exterior algebra. Normed spaces and inner product spaces are introduced in the second half of the book, and do not depend on some of the more "algebraic" sections (like those mentioned above on modules, tensors, and the exterior algebra). 2351a5e196