Hello people! This page contains some answers to questions that I am frequently asked.
Getting to Columbia
- Q: How do I get to your office?
- Q: How do I get to x at Columbia?
Coming to Columbia as a student, postdoc, visitor, researcher, etc.
- Q: I am interested in applying to a computer science or data science degree program at Columbia. Should I do so and what are my chances of getting in?
- Please see the CS department’s admissions webpage and/or the DSI’s admissions webpage. There, you’ll find information about all aspects of the application process (e.g., deadlines, your chances of being admitted), as well as information about the academic programs themselves.
- Some frequently asked questions, already answered for your convenience, can be found by clicking the following links:
- Feel free to mention my name in your application if you are interested in my research. This is a good way to draw my (and others’) attention to your application.
- I get a lot of emails about admissions, and unfortunately, I cannot write a personal reply to each one.
- Q: Do you have any internship positions available?
- I do not have internship positions available (virtual or in-person).
- Q: Do you have any open postdoc positions?
- I do not have any open postdoc positions.
- Q: Can I do a research visit with you for n months? My company/government/self will cover all of my expenses; I will not require a stipend.
- Due to time constraints, I cannot commit to hosting external students or visitors whom I’ve never met or worked with.
Getting started in machine learning
- Q: I am interested in taking a course in and/or doing research in machine learning. What courses should I take in preparation?
- First, it’s great that you are interested in these subjects! Machine learning is a confluence of ideas from many disciplines, including computer science, optimization, physics, and statistics. Because of this, it is a very broad subject that builds on a number of foundations. When getting started in machine learning, it may feel overwhelming—it did for me. At the same time, I hope it also means that there’ll be something particular that’ll “click” with you and that you’ll want to study in depth.
- A good way to get started is to build up solid mathematical foundations in
- linear algebra,
- probability, and
- multivariable calculus.
- Comfort in writing code to process and analyze data in Python is also very helpful.
- The syllabus for COMS 4771 “Machine Learning” has some resources on these prerequisite subjects.
- Q: Same as above, but particularly in learning theory…
- Q: What are good books to read about machine learning and/or learning theory?
(These are not frequent answers to questions, but rather just links to useful “advice” that others have written.)