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CS 168: The Modern Algorithmic Toolbox

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Re: CS 168: The Modern Algorithmic Toolbox

#51

Post by maayank »

sylware wrote: post #50

This is "mostly" maths for "big data" (aka statistical efficiency over huge set of data or "qsort-ized" algos) but kind of taught the wrong way: first you learn maths, then you code algos based on current hardware architecture. I had a doubt when I did realize I could not find one of the most important topics of computer science: proof of accuracy of floating point computations. I see this more like an index of a lib…

Any good resources on floating point accuracy proofs/literature?

As you say, it’s an often ignored topic while coming up again and again in software engineering

Re: CS 168: The Modern Algorithmic Toolbox

#52

Post by layer8 »

sylware wrote: post #50

This is "mostly" maths for "big data" (aka statistical efficiency over huge set of data or "qsort-ized" algos) but kind of taught the wrong way: first you learn maths, then you code algos based on current hardware architecture. I had a doubt when I did realize I could not find one of the most important topics of computer science: proof of accuracy of floating point computations. I see this more like an index of a lib…

It’s an “algorithmic toolbox” in the sense of a set of algorithms you can use as tools, not a set of tools for building new algorithms.

Re: CS 168: The Modern Algorithmic Toolbox

#53

Post by UncleMeat »

melling wrote: post #45
t_mann wrote: post #25

Earlier quoted context omitted.

> (i) ideas that are non-obvious , even to the well-trained computer scientist > In the first lesson, they discuss consistent hashing, and they seem to have achieved their goals. I was really excited when I read your comment here before clicking the link, but having had a look at the rest of the curriculum, I'm slightly underwhelmed. - Generalization ... Empirical risk minimization. - lossy compression - Similarity S…

All those topics are unimpressive? I’ve spent quite a bit of time trying to teach myself them. I’ve seen several Stanford courses (e.g CS229) so the low course number of 168 would seem to indicate that it’s taken within the first couple years of college.

At Stanford, 100 level courses are intended for undergraduates while 200 level courses are intended for mixed undergrad/grad. 100 doesn't necessarily indicate freshman/sophomore.

Re: CS 168: The Modern Algorithmic Toolbox

#54

Post by layer8 »

maayank wrote: post #51
sylware wrote: post #50

This is "mostly" maths for "big data" (aka statistical efficiency over huge set of data or "qsort-ized" algos) but kind of taught the wrong way: first you learn maths, then you code algos based on current hardware architecture. I had a doubt when I did realize I could not find one of the most important topics of computer science: proof of accuracy of floating point computations. I see this more like an index of a lib…

Any good resources on floating point accuracy proofs/literature? As you say, it’s an often ignored topic while coming up again and again in software engineering

The general topic is called numerical analysis.

Re: CS 168: The Modern Algorithmic Toolbox

#55

Post by UncleMeat »

oxff wrote: post #3

> Zero hits for "hardware". Are there algorithm courses that take into account how hardware affects algorithms? For example with databases, you have implement theoretically inefficient algorithms which are faster in practice (mostly because they use sequential access).

This area is less mature than traditional algorithms work. It is the case that cache-friendliness and minimization of data dependency chains is a huge deal for hyper efficient modern algorithms but this hasn't been the case for too long so there is a less rich history of ideas to pull from.

This course is also more about solving problems in the first place rather than implementation efficiency.

Re: CS 168: The Modern Algorithmic Toolbox

#56

Post by layer8 »

noelwelsh wrote: post #8
vrnmh wrote: post #2

Wish the videos were public too!

My (heretical?) opinion is that videos are a waste of time compared to text for the majority of content and the majority of people. There are some ideas that can be very nicely visualized and some people (e.g. dyslexics, non-native speakers) who struggle with text. For the rest I think text is better.

I generally agree, but the video linked upthread was quite nice and compact: https://youtube.com/watch?v=UF9Iqmg94tk

Re: CS 168: The Modern Algorithmic Toolbox

#57

Post by UncleMeat »

oldgradstudent wrote: post #37

How can you cover such a huge and diverse amount of material with any level of rigor? I view this type of courses as pointless. There's not enough time to cover any problems or pitfalls.

Its a class for undergrads. Nothing can ever be explored in its full depth. If people want to spend an entire semester on a single problem they should take a grad course.

Re: CS 168: The Modern Algorithmic Toolbox

#58

Post by political12345 »

buttery101 wrote: post #39

In the lecture note 6 about regularization, section 3.4: the l1 norm of a vector is simply the sum of the absolute values of the coordinates, and hence it is continuous (and linear). I don't think that the l1 norm is linear since |x+y| # |x| + |y|.

agreed.

Re: CS 168: The Modern Algorithmic Toolbox

#60

Post by gsinclair »

t_mann wrote: post #25
FabHK wrote: post #5

Very neat. They write: > In this course, we’ll be looking for the following trifecta: (i) ideas that are non-obvious, even to the well-trained computer scientist, so that we’re not wasting your time; (ii) conceptually simple — realistically, these are the only ideas that you might remember a year or more from now, when you’re a start-up founder, senior software engineer, PhD student, etc. (iii) fundamental, meaning t…

> (i) ideas that are non-obvious , even to the well-trained computer scientist > In the first lesson, they discuss consistent hashing, and they seem to have achieved their goals. I was really excited when I read your comment here before clicking the link, but having had a look at the rest of the curriculum, I'm slightly underwhelmed. - Generalization ... Empirical risk minimization. - lossy compression - Similarity S…

I am a mathematically trained computer scientist (by qualification, not by current practice) who has kept up an interest in both fields for many many years, and I don’t know most of the stuff in that list. I’ll happily accept that I’m just not all that good, but I offer myself as evidence that there might be a market for this course ;)
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