Top products from r/OMSCS

Congrats! She will need LOTS of your support, making her free time about 2~3 hrs per day would be the best gift you can ever give her for the next 2~3 years. If you don't know how to cook, wash the dishes, do the laundry, changing diaper, ... start to learn them LOL

Other than that, It depends on how much you want to spend:


- In the range of $300, I would definitely recommend a good wireless headphone (as other people mentioned already) like Bose QuietComfort. I've got one for Father's day two years ago, and that was the most useful gift I've got.Two points about headphones:
1- Even if you want to go with other brand, make sure to get the "Around the Ear" and not "Over the Ear", you will see the difference when using long hours.
2- Keep in mind some people (including my wife) are not comfortable with noise cancelling feature of the headphone and they feel dizzy or will have queasy or motion sickness feeling specially if they use it long time. You won't know it until she use it, but make sure you can either return or exchange the item. If that is the case I can recommend Bose SoundLink II.

- In the range of $500, I would definitely recommend a 30"x60" adjustable desk. There were numerous time, even if with having comfortable chair, I couldn't sit anymore! and I wished I could have stand up and continue studying! Don't go with smaller than 60" length since she has two monitors.

The book that really helped me prepare for CS 6505 this fall was Discrete Mathematics with Applications by Susanna Epp. I found it easy to digest and it seemed to line up well with the needed knowledge to do well in the course.

Richard Hammack's Book of Proof also proved invaluable. Because so much of your success in the class relies on your ability to do proofs, strengthening those skills in advance will help.

Yet another Windows user here:

I bought this Lenovo in 2015:
http://www.amazon.com/Lenovo-Y40-80-Laptop-i7-5500U-Display/dp/B00SBVIEM6/ref=sr_1_2?s=pc&ie=UTF8&qid=1440199533&sr=1-2&keywords=lenovo+y40+512

Not the most impressive laptop, but 512GB SSD is nice. Also I bought 8gb additional RAM, for a total 16gb. Intel i7

The ram is nice for running several VMs at once (tbh, that hasn't happen much in OMSCS classes). It is also good enough for running ML experiments (although I regret not buying a laptop with an NVIDIA GPU)

I love how it's very portable (14'' form factor)

YMMV, etc. In short: Windows just works.

I learned C in college with this book, but I highly doubt it's the best way to learn the language: https://www.amazon.com/dp/0131103628
I haven't used C since college, so hopefully someone else like /u/tphb3 can chime in with a better answer for you.
Edit: Just realized he said "Read K&R" (which is the book I linked to).

Needing an understanding of things such as computer networks will again come down to your course selection. How solid is your math?

thanks.

any particular python book, course, lecture video, or combination of?

I was looking for a python book, not for a beginner programmer, but for someone with already knowledge of C, C++, java. when I went through those courses, the deitel & deitel books were good, I see this one, but don't know if its the right fit. maybe so, if it will be thorough and will be a reference book later on

I presume this is the book you are referring to, correct? this

It seems pretty old, considering they have 10th anniversary editions out. Is it still regarded as one of the better textbooks out there for this subject matter today?

I bought this book to help:
https://www.amazon.com/Manga-Guide-Linear-Algebra/dp/1593274130/

And did a lot of youtube research watching videos on related stuff. This was a tough course. :)

Logitech C270

I have an older model of this and it works well enough. It's on sale for $20 as well, so that's good.

I did IOS for my first course (non-CS engineering undergrad, mostly Python and JavaScript programming, limited C). I worked through the bulk of The C Programming Language (https://www.amazon.com/Programming-Language-2nd-Brian-Kernighan/dp/0131103628) before taking the course and, on that basis, it was manageable. Hard, but manageable. I also learned a tonne and enjoyed it a lot, though the second project nearly killed me.

I think IOS is a great introduction to the program, iff you can get through some C programming beforehand...

Machine Learning, Mitchell

https://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/0070428077

Reinforcement Learning, Sutton & Barto

https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262193981

Artificial Intelligence, Norvig & Russell

https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597

7641 Machine Learning: If you're planning to use R, buy Lantz' book, and read it cover-to-cover. You'll be glad you did.

Machine Learning with R - Second Edition https://www.amazon.com/dp/1784393908/ref=cm_sw_r_other_awd_XoCGwbQPQG497

This poster


Hang in There Baby Cat Retro Motivational Cool Wall Decor Art Print Poster 12x18 https://www.amazon.com/dp/B077VXSZ8Q/ref=cm_sw_r_cp_apa_i_5Dd3Db96FP80S

At Amazon

https://www.amazon.com/Computer-Security-Principles-Practice-4th/dp/0134794109/ref=dp_ob_title_bk

If you have problems with probability take the MITx probability class on edX. That is as good as it can get as a EECS probability class. It teaches you tons of stuff but assumes nothing but multivariable calculus from you. If you have time, read Introduction to Probability by the class instructors.

Note the class alone is a huge time sink.

I'm finding this one useful for the ML class:

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

Have a look at this book: https://www.amazon.com/gp/product/B00NYBRH30/ref=oh_aui_d_detailpage_o02_?ie=UTF8&psc=1

I find the short guide on C the author has in the appendix to be very handy especially if you already know programming. Otherwise, K &R as /u/onetyone suggested.

This has been my first semester of the program, so I can't speak in general, but from the 2 courses I have done:

1. CCA - the Udacity videos could be considered as a primer / introduction to the respective topics. In addition to the videos we studied corresponding parts of 2 classical textbooks (https://www.amazon.com/Introduction-Algorithms-3rd-MIT-Press/dp/0262033844/ and https://www.amazon.com/Introduction-Theory-Computation-Sipser). Have a look into the textbooks, especially the problems after each chapter to have a glimpse of difficulty of problems solved in class. The real meat of the class were the problem sets (1 PS each week) with several quite difficult problems to solve. A grade was based on 5 exams (every 2 weeks) - each of the exams having 3 problems of comparable difficulty to solve in 90 mins (more or less) - which should prove student's mastery of the subject.
   
2. CN - the Udacity lectures constitute a skeleton of the class, which is supplemented by a meat of more then dozen of scientific papers related to the studied topics and 8 projects (half of them programming, half of them reproducing some research, doing an experiment and writing a short paper with observations). Grade is based on 3 proctored exams covering Udacity lectures and mandatory reading material (the papers) and 8 projects.
   So far I can conclude that difficulty/rigor and time required are substantially higher than just watching the Udacity videos and clicking through somewhat banal in-lecture quizzes.
   You can get some idea by looking at www.omscentral.com - there are class reviews and time requirements estimates (based on the student's experiences).
   I spent in average at least 5-7 hrs/week by CCA (weeks before the exams were more intense, others more relaxed) and ca. 2-3 hrs/week by CN. However please note that time commitment vary according to previous experience, math and CS (I don't meen SW engineering) background.
   When comparing plain Udacity with real OMSCS program - access to profs, TAs and mutual discussions with classmates make a HUGE difference in learning value.

I can offer my two cents. I’m a Googler who uses machine learning to detect abuse, where my work is somewhere between analyst and software engineer. I’m also 50% done through the OMSCS program. Here’s what I’ve observed:

Yes, Reinforcement Learning, Computer Vision, and Machine Learning are 100% relevant for a career in data science. But data science is vague; it means different things depending on the company and role. There are three types of data science tasks and each specific job may be weighted more heavily in one of these three directions: (1) data analytics, reporting, and business intelligence focused, (2) statistical theory and model prototyping focused and (3) software engineering focused by launching models into production, but with less empathsis on statistical theory.

I've had to do a bit of all three types of work. The two most important aspects are (1) defining your problem as a data science/machine learning problem, and (2) launching the thing in a distributed production environment.

If you already have features and labeled data, you should be able to get a sense of what model you want to use within 24 hours on your laptop based on a sample of the data (this can be much much harder when you can't actually sample the data before you build the prod job because the data is already distributed and hard to wrangle). Getting the data, ensuring it represents your problem, and ensuring you have processes in place to monitor, re-train, evaluate, and manage FPs/FNs will take a vast majority of your time. Read this paper too: https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf

Academic classes will not teach you how to do this in a work environment. Instead, expect them to give you a toolbox of ideas to use, and it’s up to you to match the tool with the problem. Remember that the algorithm will just spit out numbers. You'll need to really understand what's going on, and what assumptions you are making before you use each model (e.g. in real life few random variables are nicely gaussian).

I do use a good amount of deep learning at work. But try not to - if a logistic regression or gradient boosted tree works, then use it. Else, you will need to fiddle with hyper parameters, try multiple different neural architectures (e.g. with time series prediction, do you start with a CNN with attention? CNN for preprocessing then DNN? LSTM-Autoencoder? Or LSTM-AE + Deep Regressor, or classical VAR or SARIMAX models...what about missing values?), and rapidly evaluate performance before moving forward. You can also pick up a deep learning book or watch Stanford lectures on the side; first have the fundamentals down. There are many, many ways you can re-frame and tackle the same problem. The biggest risk is going down a rabbit hole before you can validate that your approach will work, and wasting a lot of time and resources. ML/Data Science project outcomes are very binary: it will work well or it won’t be prod ready and you have zero impact.

I do think the triple threat of academic knowledge for success in this area would be graduate level statistics, computer science, and economics. I am weakest in theoretical statistics and really need to brush up on bayesian stats (https://www.amazon.com/Statistical-Rethinking-Bayesian-Examples-Chapman/dp/1482253445). But 9/10 times a gradient boosted tree with good features (it's all about representation) will work, and getting it in prod plus getting in buy-in from a variety of teams will be your bottleneck. In abuse and fraud; the distributions shift all the time because the nature of the problem is adversarial, so every day is interesting.