Lec 1 | MIT 18.06 Linear Algebra, Spring 2005

I did Strang's linear algebra course[0] (link goes to Youtube playlist of lectures) several years after graduating and recommend it highly. I was looking for refresher but I gained a deeper understanding of several important concepts; in particular it's fair to say I barely understood, or perhaps even misunderstood, SVD until Strang. If you're not sure if you need something like that, I suggest doing something like testing yourself on this video[1] or the MIT problem sets[2] it's easy to tell yourself that you "know" linear algebra when it would be closer to the truth to say that you used to know linear algebra, but can't answer even basic questions today. After Strang, Golub's book on Matrix Computations is also really incredible.[3] [0]: [this video] [1]: [link] [2]: [link] [3]: [link]

If you find it easier to keep at it and learn from lecture videos instead of from textbooks, here's a math curriculum of lecture videos I've curated. This covers calculus, linear algebra, probability, statistics, convex optimization and a math for ML course thrown in for the HN audience: Calculus Revisited: Single Variable Calculus | MIT [link] Calculus Revisited: Multivariable Calculus | MIT [link] Complex Variables, Differential Equations, and Linear Algebra | MIT [link] Linear Algebra | MIT - [this video] Introduction to Linear Dynamical Systems |Stanford [link] Probability | Harvard [link] Intermediate Statistics | CMU [link] Convex Optimization I | Stanford [link] Math Background for ML | CMU [link]

I've mentioned this on HN before but I think its still relevant to people interested in learning ML who feel they are behind on the math. If, like me, you can't sit thru lots of pages of mathematics text and instead prefer that a human explains it to you via videos that you can replay, here is a list of courses that take you from basic algebra and pre-calculus math all the way to the concepts you need to understand the principles behind the most advanced ML algorithms. All explained by very energetic people who are experts in their fields, and starting from the very basics. This covers calculus, linear algebra, probability, statistics, convex optimization and a math for ML course thrown in for the HN audience: (The first two are "MOOCs" recorded in the 1970s! probably the first ever recorded MOOC, even before the internet, and the lecturer is absolute gold) Calculus Revisited: Single Variable Calculus | MIT [link] . Calculus Revisited: Multivariable Calculus | MIT [link] . Complex Variables, Differential Equations, and Linear Algebra | MIT [link] . Linear Algebra | MIT - [this video] . Introduction to Linear Dynamical Systems |Stanford [link] Probability | Harvard [link] . Intermediate Statistics | CMU [link] . Convex Optimization I | Stanford [link] Math Background for ML | CMU [link] .

It depends on who wants to hire you. For the top jobs, this is for sure not enough. I always tell people to please make sure they know all the contents from here [this video] (Gilbert Strang's Linear Algebra course) before they make any claims about being a data scientist. I bet I can teach a monkey to open a CSV in pandas and call .fit() in sklearn. But do the people really understand the underlying assumptions. Most self-proclaimed data scientists don't I am sure. I still have a hard time calling myself a data scientist. And I am three years into a relevant PhD. The more I study, the less I feel I truly know.

I don't understand this. Most of my teachers use blackboards and it's really annoying to follow a presentation like that, you have to wait for the person to write, you have no slides later on to support your notes, and since you have no slides online you have to write everything they write, so you can't even listen properly to the talk. And some stuff are just clearer on slides... I don't really see a lot of benefits in whiteboard-only lectures. Combination of whiteboard and slides are best. I can still think of some great people who don't use slides but it's rare and a few people do it well (Gilbert Strang comes to my mind[1]). [1]: [this video]