How to choose a Grad Program in Machine Learning

So, you have decided to continue your studies.

You have decided it'll be Machine Learning.

Where can you start?

Have you been pondering on how to cook a pizza? If you are anything like me, you'll consider is a hassle just to get the ingredients, let alone start making the real stuff. How about fixing that old bicycle you have in the garage?

Most tasks in life are hard because we have a hard time figuring out how to start. And a graduate program is no different. Depending on which country you live, you can find that several universities have the same program, how do you know which one is best or which one will fit you?

Machine Learning, in its current form, is a rather recent area. Because of this, you'll find that few universities offer graduate courses specifically on Machine Learning. Often, to study ML, you'll have to enroll in a Computer Science graduate program and then go with a professor who specializes on ML.

I really recommend that you focus on the professor you want to work with, rather than the University's name. A lot of people will go to good Universities without knowing nothing of the researchers there.

Doing a quick search on Google with Machine Learning and Research Lab + Country name should throw some results. It would be impossible to make a list, since there are many labs to look into, but you can look into my webpage for some insight.

Try also looking into labs that pick your interest, like computer vision, text processing, data mining, a lot of these areas are using Machine Learning. And while the lab might be using other tools as well, you can always try to improve their work using ML.

Now, do you want to do applications or do you want to unravel the mysteries of the algorithms. It is safe to say that very few people would be able to create something entirely new in a 3 year PhD, you might success at modifying an algorithm or applying some obscure test to some unseen data.

Most laboratories will look into applications, and how to apply Machine Learning algorithms, I really recommend you to look into labs that have at least a couple of mathematicians in its staff, since it will be a guarantee that their work is well established on the theoretical part.

Another thing to check is whether the professor you are interested in, is still active as a researcher, I cannot emphasize enough how important this is for a research lab, if the professor does not write papers anymore, it will be hard for him to keep up with you or whatever crazy algorithm you are thinking of.

These are nothing but some advises, and in our next post we will speak more profoundly on applications and algorithms in Machine Learning and how to choose your path.

See you next time

Remember to visit www.leonpalafox.com for my latest research and a list of ML Conferences

Do I even like research?

Disclaimer: These posts are mostly focused on people oriented towards areas such as Math, Physics and of course Machine Learning. Some of the things may not apply to other areas.

"I'm sure I want to study a graduate program.......... Really?"

You would be amazed how many times I've heard people claim they like research, when they usually don't know the first thing about it.

It usually starts with: I like to read, I like math and I want to travel. Then, they ponder how difficult it is to land a job against how difficult is to get in a Grad Program. To finally decide they want to have a PhD. Have in mind that while the labor offer is limited, the Grad Program offers are always raising.

Then, reality kicks in. In order to land a good job once you're finished -and be a half decent researcher- you'll need at least 5 or 6 journal papers, more than a dozen conference papers, and a shinning PhD Thesis ,which you'll probably hate with all your heart.

To finish your PhD on time and do all of these things, you'll need to do 3 basic things:

     Read, and I mean read. Forget your monthly book, to stay ahead and informed on the comings and goings of your topic, you'll need to read at least 1 paper each day and 1 academic book chapter every month (sounds easy?) . This will go up near conference dates, and when new specific journals you follow get published (yes, you have to follow journal publications)

    You'll also will need to write, and you'll need to balance your load of work reading with writing. Most people fail seeing this, and end up doing all-nighters to finish academic journals on the deadline, often unpolished and unfinished. I'll tackle how to handle your time in a later post.

    And finally, you'll need to do real stuff. In most scientific areas, reading is no research, is a part of it, but doing it alone won't take you anywhere. In CS you'll need to implement your ideas on code, and that'll take you more time than you would care to admit. I've spent entire coding sessions working out the bugs of my programs, let alone the real functionality of it.

To accomplish these things, you'll need a lot of self-discipline and in most cases a good advisor is also a plus. Yet, these are hard to find, and a topic I'll talk about in our next post: "How to choose a Grad Program"

See you next time

Remember to visit www.leonpalafox.com for my latest research and a list of ML Conferences

Why choosing a Graduate Program on Machine Learning?

"I wish I were in that program", "I don't like my Graduate Program", "I don't see the meaning of this".

These are some common phrases you'll hear from a fresh Graduate Student. While valid, these reasons are evidence of a dreaded truth in life. Most graduate students are lazy, ill prepared and immature people. As a graduate student myself, I consider that statement true. Not unlike anyone that has chosen the wrong job.

Most Grad students have no idea what a Graduate Program is. They think it is like college (with fewer subjects). Every time I speak with a them, I realize they have the same reasons to continue. A lack of a job offer, and liking school. And so, these kind of students clutter the research area. They often lack a vocation for research and most of the time even hate it.

Here, I'll try to help and address that issue. I'll try to give good advice on how to pursue a grad program. I'll focus on Machine Learning. I'll help you find good programs and advisors. And we will give you some tips to pursue and finish your PhD.

One of the first things you should know, is that these are mere suggestions. I'm not professor, but and enthusiastic who likes to help. I, however, consider myself humbly capable to help you decide and start. Since I've already did it with average results.

Before choosing a grad program, you have to answer these questions first:

- Do I know what research is?

- Do I really want to do research?

- Do I like math?

- Am I willing to study by myself at least 4 hours a day?

- Do I like to write?

- Am I willing to write at least 1000 words every 3 days?

- Am I willing to keep living a student life for this?

These are questions I'll be commenting as we go on. I designed them to help you find your vocation as a researcher.

If the answer to ANY of those questions is NO. I'll ask you to reconsider pursuing a graduate program in Machine Learning.

And if the answer to more than 3 questions is no, I'll ask you to reconsider a graduate program at all.

Ask yourself these questions, sleep it well, and next time we'll see how to choose a program that suits your necessities.

Remember to visit www.leonpalafox.com for my latest research and a list of ML Conferences

Lets Begin

Hello, my name is Leon Palafox.

Starting April the 1st this year, I will start my final 3 years lap to become a PhD.

As a part of a little bit of self motivation, I decided I will start writing a blog on the ups and downs of this journey, so however might be interested can have a look at this.

The final goal of this project is to have at least one publication in the NIPS Conference and to have at least 2 Journal Papers in Machine Learning related Conferences. And of course earn my PhD degree in the before asserted time.

Any help is welcomed, as well as suggestions and recommendations.

So here we start, to begin, I am watching the online course from Stanford on Machine Learning as well as reading the Bishop Book, as well I am reading the Cover book on Information Theory and I intend to read afterward the Lugosi book on prediction.

So, see you next time.