I am givin some tips for IISc - CSA dept Ph.D. interview process. The tips-list is by any means not exhaustive/complete . (Disclaimer: The following tips are my own personal opinions.. )
Pre-Interview::
1. Zero in on one/two/three research areas
2. Stick to them
3. Explore the core subjects in those areas and delve deep into them. (A bottom up approach would be ideal)
4. Be thorough with all the barebone basics and work out some problems to get a grip.
5. Climb up the ladder slowly by getting involved in more tougher areas and other hazy patches which you need further exploration.. Explore such areas with the help of your books, mentors, video lectures, online notes, etc.,etc.,
6. At each stage, get thorough with whatever you learn.
7. Discuss with "like-minded" friends about the subjects.
8. Have a checklist of what you have mastered and what you have ignored.
Strengthen your strong points even more.. forget about your weaknesses..
During-Interview :: (compose urself, be coool..)
9. During the interview, think, then answer.
10. Using the board for derivations is ideal.
11. Spurting out the answers orally will do no good.
12. Even if you get a small hint to the answer and if you are really confident, just tell the hint. Complete answer does not count in such cases.
13. Nobody is gonna ask you to prove a tougher theorem / derive a very hard result. instead, they focus on your thought process for many problems. For simpler problems, they expect the right answers from you. (ideally you are expected to know the basics better)!!
Post-Interview::
14. Had you done well in the interview, jus wait for the call..
15. If not, go back to step 1 and redo things more elegantly the next time..dont be depressed in any way..
All the best for your efforts and hope this blog was useful !
Since i was quizzed on Data Mining/Machine learning, i could tell you some basics to cover before you gear up for the interview ::
1.****Linear Algebra(Matrix Theory,Vector Spaces)
2.****Probability theory(the full theory including pmf,pdf,cdf,basic prob. laws,conditional probability, distributions(discrete,continuous), laws of large numbers, markov & chebychev eqns, etc.,)
3.***Optimization(if you have already studied, else you may just know the basics like convexity, duality, KKT conditions, etc.,)
4.**Discrete Maths (Groups,Rings,Fields,Sets,Relations)
4.*Basic algos,data structures
5.*Data mining basics
*:indicates the weightage you may give to each area
(In part, this blog was inspired by a similar and a detailed blog by Ramakrishnan Kannan of CSA)
