Data Science: CSCI E-109 Publication Listing

https://matterhorn.dce.harvard.edu/engage/ui/index.html#/2016/01/14328

by tempw • 10 years ago

284 19 10 years ago

matterhorn.dce.harvard.edu

minimaxir2• 10 years ago

The URL was changed from http://cs109.github.io/2015/pages/videos.html , which has more information and links to the corresponding repositories.

bertomartin• 10 years ago

Really great, would be cool if it were on youtube though

vinay427• 10 years ago

Some may find that useful but the video player used here (as with the one used for lectures at my university) supports far more relevant features such as browsing by slides and changing the layout of simultaneous video streams.

rovr138• 10 years ago

Like vinay427 said, I like the player here.

jamesblonde• 10 years ago

Looks like a kitchen sink course. The deep learning lecture tried to summarize all of deep learning into 1 hour. Didn't do it for me.

minimaxir2• 10 years ago

There's nothing wrong with a kitchen sink course. Mostly likely the in-depth deep learning classes have their own course.

i__believe• 10 years ago

This is how I prefer it. Give me the lay of the land, if you will, then I can dive into what is interesting.

SamPutnam• 10 years ago

I found the lecture useful. I haven't made it to the end, but there is a good visualization on solving the XOR problem without the Kernel Trick, starting at 26:57. She shows a multi-layer perceptron and uses two hyperplanes (hidden layer) to write a remapping so that a single hyperplane (output layer) can classify the 4 points. It is a general perceptron example, so it's only one hidden layer, but it shows the gist of DL (transforming data into a new representation) in a way that I hadn't seen before.

makmanalp• 10 years ago

I'm taking this now, this year they split it into two classes, and so now there's much much more time to go into theory. But this is still an applications-focus class. They teach you general stuff: What to model, how to build, evaluate and improve models, how to do this on real world data, common pitfalls, how regression and classification works, then some specific models, tradeoffs and similarities between those models, then some methods to improve results from those models.

But this is great, because if you want to learn more in depth about those things, you can just take the second class in the series or a more advanced classes, and / or pick up a book, and you're already at a great position to improve.

fdgdasfadsf2• 10 years ago

Does the "1" in "109" imply that this is a intro course?

davidwihl4• 10 years ago

No, those would be two digit courses. 1xx courses are typically for either undergrad or grad credit. 2xx courses are typically for grad credit. CS50 is the classic entry level CompSci course at Harvard.

https://www.seas.harvard.edu/academics/courses/computer-scie...

sn9• 10 years ago

The only prereqs for this course are an introduction to programming course and an introduction to statistics course, so this isn't surprising.

davidwihl4• 10 years ago

My project is at 28:00 of the final project presentations. We won overall that year. I'm TF (teaching fellow) for the current course and Head TF for the advanced course (CS109b) in the Spring. AMA.

faitswulff• 10 years ago

For the lazy, it's chanceme.info, a data-driven college admission prediction project - that is really neat. Unfortunately, I'm getting a "Error: HTTP error 404." on the result.

davidwihl4• 10 years ago

Looks like an old DNS name expired. It has now been renewed.

Data Science: CSCI E-109 Publication Listing

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