Stanford Computer Science Masters Requirements

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A year and a half ago, I was accepted into a CS-related graduate program for professionals at Stanford and finally graduated in December. Here is my shiny new certificate. In total, I took 4 courses and it took me 5 quarters to complete (I took a summer break). In this post I will introduce this program if you are interested.

Stanford Computer Science Masters Requirements

SCPD stands for Stanford Center for Professional Development. This is their website. They offer courses on subjects ranging from computer science foundations to cyber security strategy. I enrolled on the Artificial Intelligence programme, which included courses in the Computer Science department.

Department Of Computer Science

In this and many other SCPD programs, you simply take classes with other Stanford students; you listen to lectures (online), submit assignments (written answers to math problems or computer code), get grades, take exams and midterms, and discuss your problems with teaching assistants.

Each course takes a quarter to complete. I completed 4 courses in 5 terms because I wanted a summer break. I’m sure there is a common term, but I can’t find any reference to it at the moment.

Each program usually offers several courses and you must complete a specific set of courses. For example, the AI ​​certificate requires completion of 4 of the 14 offered through the AI ​​certificate: one required course (CS221 Artificial Intelligence: Principles and Techniques) and three optional courses. The range of courses offered is updated to keep up with the times.

You do not need to complete the certificate program. in fact, you can take classes that interest you and have an easier time with grading and assignments. You’ll still have to go through similar registration steps, but the expectations will likely be lighter.

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If you complete the programme, you will receive a paper similar to the one above. And if you mention him on LinkedIn, you’ll have a Stanford icon next to your profile 😉

Expect a lot of hard work if you’re going to actually get certified (if you’re only taking some courses, choose your own expectations). The requirements on the certificate page are strict, so please review them.

There are many guides, there is Towards a Data Alphabet, there are free books and videos online… But there are things you can only get through supervised education at a reputable institution.

If paying for things or spending $5K without doing some research isn’t your thing, you can get a lot of that education for free, or at least try the course before you buy it.

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It’s crazy how much of this stuff is available for free. As here are the full lecture notes for CS231n. There are lecture notes for other classes as well. The lecture notes for CS229 are valuable information about the foundation book for “classical ML”.

And of course, there are other resources besides Stanford where you can learn about ML. Consider a Google Course. It is much more focused on practice and you will avoid all the maths. If you accept it and feel like you are creating more math and are ready to learn these concepts at a deeper level, then move on to more advanced programs. If not, go ahead and hack the awesome ML-y stuff.

Now, I know, it’s hard to beat a Stanford certificate. Few schools have the qualifications, reputation, resources and faculty that Stanford offers. If you’ve never been to Stanford University and you live nearby, you should check it out. It even has a Rodin sculpture garden.

However, if you live near, say, another good school that offers a similar program, consider applying to that school instead. There are definite advantages to face-to-face communication with CAs and other distance learners.

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Yes, SCPD policy prevents you from attending lectures and tests on campus. Although you are not “guaranteed a place” in the exam halls, this has never been a problem for me. I always showed up and found a place. Teaching assistants and teachers were always happy to talk in person rather than playing around with Zoom or some other remote conference. Although, once I drove to campus only to find my CA at home video conferencing via Zoom.

Also, some Poster Sessions where you exhibit and present a poster for your project are essentially huge career fairs. CS231n poster session “Convolutional Neural Networks” was particularly one. Waymo, Zoox, Cruise and a host of other companies located their recruiters there.

The same can be said for other industry events. By all accounts, CVPR has become not just a careers fair, but a full industry ML expo as well as a sort of scientific conference.

No, you cannot have a student ID and therefore will not be eligible for discounts. However, you will have access to the digital library and will be able to download all the papers you need for your work from ACM, IEEE, etc. and other sites for free.

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And you will receive an email throughout your studies. Use it while you can.

Since machine learning requires a lot of GPU time, you’ll earn credits on Microsoft Azure or Google Cloud to run GPU-intensive calculations for class assignments and projects. But if you have a gaming PC at home, you don’t need to do this. Last year, while I bought a new GPU, I managed to get rid of my old GeForce GTX 1070 for the most part, which you can buy new for $300 (it’s very old as far as machine learning goes). Remember, you spent $20,000 on training, so you can definitely save $300 for the GPU.

Even if you don’t have a gaming computer and only have a non-gaming laptop, consider building an external GPU setup like I described in a blog post a year ago.

Of the AI ​​certificate courses, here’s what I took (in the order they might be of interest to you), with a more detailed description to follow.

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CS231n Convolutional Neural Networks for Visual Recognition is the best course I have taken on deep learning and neural networks. If you are interested in hands-on deep learning, take this course first. Registration for this course is open at the time of publishing this blog post, so don’t miss your chance! You will not only learn how to work in computer vision, but you will also learn the fundamentals of deep learning, from manual acquisition and input and gradient back-accumulation, to using proven architectures for basic computer vision tasks (including the details nitty-gritty like building stimulus maps).

Although this course would have been cutting edge 5 years ago, it felt a little dated to me. I would not take it for the newest updates on the subject, but more for the basics.

Course covering a wide range of AI topics including Approximate Graph Search Algorithms (star A), Reinforcement Learning (with a fun Pac-Man challenge in which I placed 4th), Markov Decision Processes, Constraint Satisfaction Solvers and other basic tools. “classic” AI without much ML. If you think this course is easy because it’s an “overview” course, don’t be fooled. You are expected to dig deep and show understanding

The course was taught by Percy Liang; he is an excellent teacher and researcher; Indeed, our CS224n course project (see below) was an extension of his work. 🙂

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Pac-Man Contest Leaderboard featuring my submission 5 days before the deadline… 🙁 If you get a high score, don’t reveal it.

CS224n Natural Language Processing with Deep Learning. I learned about regular neural networks, LSTM, word vectors and neural language models. In two of the tasks, you will implement part of the Neural Machine Translations and train an encoder-decoder network to translate English into Spanish. You will do this once with word embeddings and another time with CNNs. This is crazy!! These were the most amazing achievements in the world 10 years ago, and now you can do it

This is a fairly new course and it represented the first grade in NLP when I took it a year ago. If you are interested in NLP, this course is a must. Of all the courses, this was the best taught, the most relevant and the most memorable for me.

AA228 Decision Making under Uncertainty. This is a combined CS/Aeronautics course that involves modeling and working with uncertainty in decision making and planning. This is a fairly detailed course, unlike those listed above, and focuses almost entirely on planning, Q-learning / SARSA, Monte Carlo tree search methods, and partially observable Markov decision processes.

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The most useful result for me was that I finally understood how to use Bayesian inference in a practical context. This course starts with the basics, and the instructor takes the approach “if you don’t really understand the basics, we might as well not talk about POMDPs.” Finally I can work with conditional probability and now I can contribute to mission objective modeling at work. Individuals with an online master’s degree

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