Help Advance the World with Advanced Linear Algebra
hornraiser.utexas.edu
hornraiser.utexas.edu
Harvard and UC Berkeley used to have an excellent OCW sites but they migrated a lot of content to EdX or elsewhere and removed it from their OCW sites. Stanford also took a lot of stuff off of their multiple OCW type of sites and moved it to Coursera or elsewhere. Thankfully, MIT still kept much of their stuff on their OCW site even after adding it to EdX.
Ten to fifteen years ago a lot of universities had sites imitating MIT OCW but now very few do. The trend has been to move very good freely available notes, etc., off of faculty webpages to behind Blackboard or similar logins; now there is little left that is publicly available and easily accessible without having to login to some site (or be tracked).
I spent a lot of time searching for reasons why universities went away from OCW. Apparently, people with hearing disabilities sued universities to get transcripts added to OCW courses that had videos on the grounds that lack of transcripts left them at a competitive disadvantage. This didn't make sense to me: many OCW courses had great course materials but no videos; courses with videos could simply release one video at a time with the next video held off until the general public / free users / people like me completed a transcript on a wiki type site of the previous lecture. (For some of those course materials, I would have gladly worked on transcripts on a wiki for free!)
In case there is anyone out there that isn't aware of any of these:
* https://www.oeconsortium.org/
* https://ocw.mit.edu/index.htm
* ...
Control freaks always want your personally identifying data, and people are too open to giving it out. HN is one of the greats because they are not such control freaks.
Some other free Linear Algebra courses:
https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra...
There's a few more out there too.
The college needs $75k of donations to be able to put the curriculum on edX?
They have already made courses and made them available online. It was a massive undertaking. It is not too much to ask for their effort to be funded at the modest level they have requested.
That said, it is counterproductive to make that case in the instance where someone is trying to use public money to provide a public good. The correct place to protest is when the university sends you your tuition bill or when they decline you admission. The instructors have no access to or control over the billions in their university’s endowment fund.
Right now, our options are to either fund this effort or not to fund it. I hope that we fund it.
Your "protest" over a (1) tuition bill or (2) admissions rejection is not going to matter. You'll be sent to collections in the case of (1) or ignored in the case of (2).
Publicly funded universities should contribute _something_ back to the public. Generally it's fucking football or basketball (assuming you pay for the right TV service).
You are barking up the wrong tree.
:)
It wouldn't cost that much as a % of public education costs.
Khan Academy claims to be doing this, but their videos are not particularly high quality.
They're asking us to crowd-funded a crappy profit-making scheme.
The course will be of questionable quality. You don't learn math without doing math. That's a scientific fact. Their "free" offering is "audit videos." I'd encourage anyone to look up the mass of literature about interactive engagement versus passive learning. You feel like you're learning, but you're not. People basically would waste their time.
Let’s add 80 hours of technical nuisance to make the videos work nicely too.
That means that the effort would be worth $268/h roughly. If the course was very good, I’m not against a teacher making that for a particular project.
Calling it donations is weird though, I guess another commenter said it was the university that was requiring it
The amount of money floating around in higher ed., it seems in poor taste to need a ‘donation’ to make this available to the public at large, from a public university, funded by taxpayers.
It could be marketed better.
The greatest failing in my math education is that it didn't give me the "keys to the kingdom" in other words, it didn't teach me how to conceptualize problems and really dig into literature to further my understanding on topics as they come up.
Contrast that with my signal processing courses, which were very much focused on concepts, conventions, notation, and best practice to provide a ground work for future study.
And working in the signal processing field, it was easy for me to dig into literature as I researched problems up until my math skills failed and I had to teach myself a lot of what my grades showed I had learned in college. Linear algebra in particular.
So when I see something like a course that will teach me about orthagonality, linear systems, SVD and eigenvalues I want to back it. But only if it's the kind of content that will tell me what those things are and how to read about them and understand them, not more rote memorization of how to work a problem set.
Just my two cents.
Probably not as deeply as this course proposes to be sure, but good into for those interested.
Part I: SVD = bread and butter in industry with lots of applications in ML and stats, engineering stuff too, see https://www.youtube.com/watch?v=R9UoFyqJca8 )
Part II: Solving Linear Systems = 70% of science can be describes as A*x=b where A is a matrix and x and b are vectors. Spoiler alert: the "find the RREF algorithm" that we learn in first-year LA is not the most efficient (or numerically stable) option.
Part III: Eigenvalues "for real" this time = e.g. iterative solution methods that scale. The flagship application of this idea would be the initial PageRank algorithm that works on the adjacecy matrix representaiton of the graph of all webpages (millions of columns, see http://infolab.stanford.edu/pub/papers/google.pdf )
So to summarize, this course covers all kinds of topics that you don't need to know if you want to apply LA to small and medium data, but for large scale stuff would be really good to know.
Since they haven't started the course yet, I doubt anyone has direct experience,
but the curriculum for the fall 2019 fall offering is (from their home page [1] )
1. CS 391L Machine Learning Adam Klivans and Qiang Liu
Computing systems that automatically improve their performance with experience, including various approaches to inductive classification such as version space, decision tree, rule-based, neural network, Bayesian, and instance-based methods; as well as computational learning theory, explanation-based learning, and knowledge refinement.
2. CS 380L Advanced Operating Systems -Vijay Chidambaram
Study of the formal structure, design principles, organization, implementation, and performance analysis of multiprogramming and/or multiprocessor computer systems.
3. CS 380P Parallel Systems - Calvin Lin and Chris Rossbach
The objective of this course is to provide students with strong background on concurrency fundamentals along with experience with a diversity of both classical and modern approaches to managing and exploiting concurrency, including shared memory synchronization, parallel architectures such as GPUs, as well as distributed parallel frameworks such as MPI and map-reduce. Material will be presented through readings and discussion of background material as well as occasional recent research papers when appropriate. The course requires a number of programming and project assignments to provide direct experience with design, programming, and measurement methodologies for concurrent systems.
4. CS 395T Optimization -Sujay Sanghavi
This class covers Linear Programming and Convex Optimization. These are fundamental conceptual and algorithmic building blocks for applications across science and engineering. Indeed any time a problem can be cast as one of maximizing / minimizing and objective subject to constraints, the next step is to use a method from linear or convex optimization. Covered topics include formulation and geometry of LPs, duality and min-max, primal and dual algorithms for solving LPs, Second-order cone programming (SOCP) and semidefinite programming (SDP), unconstrained convex optimization and its algorithms: gradient descent and the newton method, constrained convex optimization, duality, variants of gradient descent (stochastic, subgradient etc.) and their rates of convergence, momentum methods.
5. CS 395T Deep learning -Philipp Krähenbühl
This class covers advanced topics in deep learning, ranging from optimization to computer vision, computer graphics and unsupervised feature learning, and touches on deep language models, as well as deep learning for games.
(Spring 2020)
6. CS 395T Advanced Linear Algebra for Computation Maggie Myers & Robert van De Geijn
Linear algebra invariably lies at the core of techniques that are of critical importance to computational and data scientists. In this course, you learn advanced concepts in linear algebra, practical algorithms for matrix computations, and how floating point arithmetic as performed by computers affects correctness.
7. CS 388G Algorithms: Techniques and Theory Vijaya Ramachandran and Greg Plaxton
Sorting and searching algorithms, graph algorithms, algorithm design techniques, lower bound theory, fast Fourier transforms, NP-completeness
8. CS 395T Online Learning and Optimization Constantine Caramanis and Sanjay Shakkottai This class has two major themes: algorithms for convex optimization and algorithms for online learning. The first part of the course will focus on algorithms for large scale convex optimization. A particular focus of this development will be for problems in Machine Learning, and this will be emphasized in the lectures, as well as in the problem sets. The second half of the course will then turn to applications of these ideas to online learning.
9. CS 394R Reinforcement Learning: Theory and Practice Peter Stone and Scott Niekum Introduces the theory and practice of modern reinforcement learning. Reinforcement learning problems involve learning what to do—how to map situations to actions—so as to maximize a numerical reward signal. The course will cover model-free and model-based reinforcement learning methods, especially those based on temporal difference learning and policy gradient algorithms.
assuming these are the equivalent of the offline courses, in terms of instruction, exams/assignments, and have decent instructor access, if not at quite the level of an on campus education, looks solid enough to me.