...on how to do a PhD, so mainly that you don't repeat my mistakes. (train passing by) Train.

Green Screen  0:12

So you've made it into a PhD program, congratulations, you made it. So today we're going to have a look at what to do during a PhD, how to succeed at publishing papers, how to deal with reviews, what to do at conferences, and many other things. So I hope you enjoy this little guide on how to survive a machine learning PhD in 2021.



Green Screen  0:45

So first of all, let me say, I'm not good at this. I'm not an expert. I'm at the end of my PhD, and I've done many things wrong, and by no means am I a successful academic. However, if you're like myself, and at the beginning of your PhD, you don't really have a clue what to do, you don't know how to select topics, you don't know how to write papers, or even what a paper is really, then there might be something in here that could help you. I'm not super successful myself

Outside  1:14

but what I can tell you is that, I've seen many people who are good at it. So, I can tell you what those people did right, what I did wrong, and generally what I think you should do.

Green Screen  1:28

Alright, that being said, let's dive right in.

White Board - (Green Screen)  1:31

When it comes down to choosing a topic, make sure you look for something that your advisor or the senior people around you have lots of experience in, they can help you much better like this. You also want to choose something that matches your particular interests, because you're going to be stuck with it for a while. Lastly, you're going to choose something that fits your expertise where you're already reasonably good at or can get good at very quickly. At the intersection of those three things, you're going to find something that is unique to you, and is going to be a very good topic for your PhD.

Green Screen  2:06

But there are a few more things to consider when selecting a topic. First of all resources, how much access to resources you have, will determine what kind of topics are even accessible to you as a researcher. So I'm going to assume that you do not have a giant compute cluster or heaps of money around. And therefore my recommendations are going to be for, let's say the rather average PhD student who is not a giant tech company. However, if you do happen to have thousands of TPUs in your backyard, you can ignore my advice and just train big language models. Alright, there are two fundamental ways how you can choose a topic.

Hype Topic - (Green Screen)  2:50

Way one is to choose the biggest, most hyped topic in the area right now. Now, that is not necessarily a bad strategy but it has some drawbacks. And the reason is that in a hype topic, there are many papers, but there is also a giant amount of competition, not only from other researchers, but from large corporations with lots and lots of resources behind them. And the bigger reason why it's a bad idea is

Hype Topic - Outside  3:19