Phd machine learning reddit. All of this assumes your primary research is in deep .

Phd machine learning reddit My initial post-PhD ambition was to delve deeper into machine learning research at leading tech companies such as Facebook, Google, or Microsoft. Those are all great schools. For this reason, fewer Why avoid CS for AI: There’s no denying that machine learning is popular and many PhD positions today revolve around AI. I am interested in research internships at some of the big tech companies, and there are just some questions that I'd like to ask the reddit community: A Ph. I’d go for a statistics PhD if you wish to get into the Mathier side of machine learning. Or check it out in the app stores &nbsp; &nbsp; TOPICS. I know this is a little disorganized and at times vague and abstract. DeepMind). I am currently a senior in high school, and I want to pursue a PhD in machine learning. I want to do a PhD in the long run, but I want to be fully prepared, armed with a solid knowledge of the basics and good experience in the field before taking my 5-year plunge. I was promised a PhD, the opportunity to do cutting-edge research, real world applications and a "close" cooperation with industrial partners. I'm hoping that stronger mathematical foundations will help Honestly I'd say doing research in machine learning is mostly about the data and code optimization and not so much about the models, not unlike working as an engineer, but maybe I'm an outlier. I am doing a PhD in machine learning but my field is heavily based in computer vision and also some techniques from natural language processing, so I'm mostly doing deep learning. If you want to improve your knowledge and build your resume, a master's will help. Economics PhD student learning machine learning . And I don't think that you made a View community ranking In the Top 1% of largest communities on Reddit. Building a strong foundation, hands-on experience, and a commitment to staying informed will empower individuals to navigate the complex landscape of machine learning successfully. YMMV, so talk with your adviser). I originaly wanted to do MIT, Stanford and Berkeley, but only Stanford has a public directory of their CS PhD students. Hello, I would like some review for: George Washington University (GWU)'s ONLINE PhD in Artificial Intelligence and Machine So I was talking to my advisor on the topic of implicit regularization and he/she said told me, convergence of an algorithm to a minimum norm solution has been one of the most well-studied problem since the 70s, with hundreds of papers already published before ML people started talking about this so-called "implicit regularization phenomenon". I am a machine learning PhD student in a UK university. Dual GPU machine is a minor plus for prototyping large models since you can debug multi-GPU code. The typical advice I see on more general academic sub like r/AskAcademia is that you should do a PhD only if you want a career in research, but I decided to post here because I get the impression that there is a smoother continuum between "research" and "industry" in machine learning (and maybe even in CS more broadly) than in most other fields I'm a PhD student in the US right now. Usually the first months are spent on learning the tools, tools which are not useable outside of the faang anyway. I work as a Machine Learning Scientist for a start-up, which in theory means I get to do research in ML. PhD in Machine Learning at Cambridge vs UCL I'm in the very fortunate position to have offers for PhDs in ML at both Cambridge and UCL, but am struggling to choose between them. "PhD level" is pretty broad. Master's thesis. 20 votes, 34 comments. I don’t know what “in this day and age” means but the only red flag is if recruitment of an apprentice (PhDs are meant to be learning experiences) requires an accomplished career. ml. D. I find it gets new PhDs up to speed very quickly. I plan on majoring in math and statistics at a T50 state school. IMO top 5 Ph. As a postdoc I'm getting about $50k. Most universities have some kind of computing centers where some GPU power can be arranged. I'm interested in learning machine learning, in a way that is suited for someone with my background experience. How is your abstract thinking? If you are comfortable grasping funky concepts or don't mind seeking out visualizations to illuminate weirder concepts (neural nets) then I'd say your 2) Computer Science is way more than just learning to write code. You dont need a phd to make good money doing ml/datascience/math. I have done a master in mathematics before doing a PhD in (applied) machine learning. Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back. 415K subscribers in the learnmachinelearning community. The question is clear. Options 2 and 3 may be compatible to what you want. At the conclusion of the The key here is, what problem would you like to be known solving that promotes your personal brand. As a father who also needs to work, I am a machine learning engineer working in research at a university. Lots of backup options like being a professor or depending on the PhD, work in many different things like a machine learning specialist at FAANG or something. I've completed ~300 problems on Leetcode, and 156 out of the 254 problems from Facebook tagged list (recent 6 months). I've worked on research in the past, and I'm specifically interested in domain adaptation/synthetic data generation. In theory, what I really want to do is figure out next-generation learning algorithms, especially those motivated by principles from the brain. Get a good job or idea in one of those fields instead. Now, here's the dilemma: I'm torn between starting with a Currently, someone with expertise in machine learning, even at the undergrad level, can get a good quality and high-paying job right out of undergrad. relevant internships, relevant published work). gatech. " View community ranking In the Top 1% of largest communities on Reddit. But most of my interest was for the mathematics behind Machine Learning and AI. I know this question might sound very strange and maybe daunting, but I love this community so I'll give it a shot. Don't focus too much on your future career at this stage. Only do a PhD if you HAVE to, if you’ll wonder your whole life if you could have done it. Reply reply Dear r/MachineLearning, I am a STEM graduate who became interested after my Master's into making research in Machine Learning. Everything has been done in house and is custom. e you were top of your class in Stanford/Oxbridge/MIT etc and you did your PhD on some of If PhD(Machine Learning) or PhD(Statistics) is the only line on it in your resume you're wasting it. 17 votes, 12 comments. Also in the UK, it is expected you to finish a Master's degree before a Ph. ETH) and Chinese universities (e. That said, if you're absolutely set on doing a PhD or a graduate degree in machine learning, you're already on the right path. It really depends on your advisor, the source of funding, and your motivation. Lots of non-researchy folk are outright intimidated by research; Not all PhDs bother to learn productionization rigor. PhD. In that respect I have total freedom to use my best judgement and try out new ideas. g. Machine Learning PhD Applications — Everything You Need to Know by Tim Dettmers. After you see that project through, by all means follow your own ideas. Third, I have never in my life seen colleagues preferring to hire someone with several top publications over an intern that did a great job. github. ) Choosing a direction since it might lead to a better outcome without being fully committed to it is a bad idea since an That's when I decided I'd go back to uni for a PhD (basically for shits and giggles - there's no monetary benefit in having an academic degree in my line of work). A lot of my mentors were math/physics majors who later came into machine learning and so perhaps my idea of "what it ETA: I tell everyone not to do a PhD. It can be a learning experience, but don't underestimate how often it is not. I’ve reached out to a few people, but not sure who to talk to now. However at top schools you would need strong grades and usually some sort of past work in research, or that at least falls close to research, you could point to on your application + strong letters of rec. Hi I'm a senior machine learning engineer, looking for for buddies to build cool stuff with! /r/Statistics is going dark from June 12-14th as an act of protest against Reddit's treatment of 3rd party app developers. It is ridiculously competitive, especially for Machine Learning, and I think I got very, very lucky. View community ranking In the Top 1% of largest communities on Reddit. If you don't want to do research like develop new algorithms, don't do a PhD. And most of the ML projects are just programming on keras and stuff. it helps to have at least some things on your resume that might catch the recruiter's eye (e. enjoy puns try and consistently 1up an idea by improving it bs a lot with friends and foe enjoy engineering from all walks of life sample more creativity, read, watch, listen increase your tastebuds learn first hand - don’t delegate understanding TL;DR: Want to do NLP and machine learning research but I've been rejected from every PhD program I've applied to (3 different seasons). Get the Reddit app Scan this QR code to download the app now. I have some experience in ML, although I believe I have much left to learn (hence my desire to pursue a PhD) A (US) PhD program is a ~5 year training program for a research career. All of this assumes your primary research is in deep Inspired by this post, I decided to compile the same data but for CS PhD students, because I'm neurotic. Someone that you like because you're going to spend many difficult moments with that person. So, OP here may have gotten a PhD in Mathematics, or a PhD in Machine Learning. It was the closest opportunity that I can find to getting ML research experience. r/MachineLearning. View community ranking In the Top 5% of largest communities on Reddit. If you have more detail on what you actually did, then the formal title will be less important than the topic and surrounding content. For a PhD applicant, I'm making the commitment to hire you and keep you employed for five years no matter what. Link posts must include context (ie: a comment in the reddit thread linking to the paper/code), and either be demos of novel The most important part of CS for ML (in my opinion) is learning to program, which is not the goal of a CS degree. I posted in bioinformatics subreddit to ask what they thought and a decent amount of comments said I do not have the necessarily background to do machine learning research. The reality is less democratic: mastering ML requires gritty, systematic work best learned through formal training. 3- I want to get into one of the top 20 US PhD programs for machine learning, problem is that these require me to have contributed genuine research to the field and from the looks of it that'd be impossible right now. Depends heavily on the PhD program. 3) While it seems to be a popular narrative, Machine Learning is far more than Statistics. $300k+ starting salary if you're a PhD grad from one of the top research groups in the world, and land a gig at DeepMind, FAANG, etc etc etc. Examples of good PhD theses involving machine learning? I'm just getting my feet wet in machine learning, and also starting a PhD in computer science. Coursera Machine Learning is good but I feel the notation on neural networks is somewhat convoluted and it's taught in Matlab/Octave (which can be alright depending on your background, but it was a bit of a minus for me). One of our best PhD student has a job at a company that is developing the same tech she is using in her PhD research. The project that I'm working on, while not about machine learning directly, will involve a fair bit of data analysis, in As for learning opportunities, everything you can "learn" as a PhD student you can learn now too based on your background. Lately, I've been feeling like, in order to make real progress in Machine Learning research, I need a much stronger mathematical background. For our sub-specialty, we got 500+ applications for <10 spots. I was thinking I would do it in order to facilitate becoming a machine learning engineer. to learn ML (Doctoral Degree)? Hi, what are the top universities in the U. By "machine learning" I am referring to the standard possibilities ( things like image recognition, face detection, voice to text, translation and so on . That's a really high bar, and it should be if I want both of us to be successful. Not everything works in a PhD, the point is what you learned by doing it. Here, you can feel free to ask any question regarding machine learning. _This the first such job is the hardest. Demystifying ML PhD Admissions to US Universities by Hima Lakkaraju. Most have 5+ papers. CSCareerQuestions protests in solidarity with the developers who made third party reddit apps I did a research internship once with two advisors: one of them had a PhD and the other didn’t. I still think it's a good course, but it's also important to try out different resources. As with most things, most places won't specify a particular background for roles, but it should be fairly clear how spending a year of dedicated time understanding machine learning should give A place for beginners to ask stupid questions and for experts to help them! /r/Machine learning is a great subreddit, but it is for interesting articles and news related to machine learning. I've been looking for a PhD in machine learning/deep learning/computer vision/etc. Only a few explicitly will not. 164 votes, 77 comments. We had a small chat, and the way What are the most paying jobs in machine learning depending on their applications (if there even is a difference in salaries between them)? (new grad PhD) is USD ~300K/year (includes annual RSUs, which is basically cash). There's an overall preference for CS graduates but I've found it's helpful to have I'm not saying that I don't understand why there's any experimental research in machine learning at all, especially when it comes to applications, but that it's strange that the sort of research whose point is "look at this new algorithm/architecture" is substantiated with "it does well on MNIST" instead of "it has xyz theoretical guarantees. /r/Statistics is going dark from June 12-14th as an act of A subreddit to discuss political science. However, most of those papers are junk!. This could be as a machine learning engineer, data scientist, or research scientist at any relevant company. A subreddit dedicated to learning machine learning Hello everyone, PhD student who is following a PhD program in Operational Research with focus on Machine Learning. for a Ph. Once you go deep in research, you might prefer some other directions (academia, more applied industry, startups, etc. _This community will not In the UK there are at least 3 types of PhDs: 1) PhD by thesis, 2) PhD by publication and 3) profesional PhD. However, I understand that the top programs (MIT, CMU, UC-Berkeley) are extremely hard to get into, even more so for machine learning. Some are first year, some are fifth year students. I found this information by looking at the student's personal webpage, where they stated their alma mater on the site itself, or on a CV provided on the If you're looking at posts like generic "Data Scientist/Machine Learning Engineer" role #3489 at most companies, and they are just asking for "Masters/PhD preferred" without domain specified, then these places are more using it as a broad filter, not necessarily requiring a grad degree IN machine learning and competing over that same smaller Definitely write up what you did and do a comparative study. Starting comp for top caliber PhD machine learning researchers at the most competitive tech companies (including equity grants) is $350k-500k and it goes up from there. for a while, but I don't seem to find many offers. See the Curry- Howard correspondence for example. What's a good starting point for someone with a PhD and 1-2 years of experience? I'm switching from academia (postdoc) to industry. I was wondering if you could give me some advice on negotiating a salary for a senior machine learning/ software engineer in San Francisco. I know reddit likes to pretend like Europe is some sort of magical utopia but IMO the PhD experience in the US is better, at least at a top university. I want to do a PhD in developing machine learning methods relating to biology and medicine. You can do even sophisticated models in your basement. Most small companies don't do research. Most CS undergrad students take it their senior year and if you want to learn more, it's taught in a masters/ph. Machine learning is a rapidly evolving field, and keeping abreast of new techniques and advancements is essential. As there's a budget in my PhD studentship for equipment, I'm thinking of buying a desktop that I can SSH into from my laptop. I would think that for getting a PhD in machine learning , you need to come up with a good piece of research, normally in the form of a new algorithm, a new theorem, a revision of an old algorithm I am a Master's student in EE from a pretty decent school, specializing in Image Processing and Machine Learning. CSCareerQuestions protests in solidarity with the developers who made third party reddit apps. A bad GPA (without justification) / 0 research experience / lukewarm recommendation letter will absolutely destroy your chances, but 3 papers is a bit much. . I think the older ones took software engineering jobs back in the day. A place for beginners to ask stupid questions and for experts to help them! /r/Machine learning is a great subreddit, but it is for interesting articles and news related to machine learning. D in machine learning and AI seems unnecessary to get if your goal is to be in some leadership position in the industry. Many others have dropped out because work got too much in their way. There are people who obviously go into applications of ML and join corps but if you want to join as Lead ML/DL engineer or want to lead teams then it makes sense to do a PhD. you do not need to learn something in depth ahead of time before starting a phd. Your prototyping machine does not need to have the most powerful GPU, but should have sufficient VRAM for the models you want to run. Demystifying PhD Admissions in Computer Science in the US: a Some say a PhD will only give you precious knowledge on the topic to continue your career as a researcher/professor and it's not worth it if you plan on working in the industry. I have a strong interest in machine learning and have completed many computing courses as well as independent projects in this field. The salary differences are insignificant. Leaving aside the UK for a moment (where perhaps there are a bit more options, but not that many), the options in the continent seem to be very rare. ) we have today. This subreddit is temporarily closed in protest of Reddit killing third party apps, see /r/ModCoord and /r/Save3rdPartyApps for more information. They both came from the same machine learning lab. edu. Also, see r/OMSCS. Hi everyone, I am a fourth year PhD student in economics (to be more specific, empirical industrial organization). I marginally prefer the supervisor at UCL but am wondering if that's outweighed by the prestige of a Cambridge PhD. Pick whichever of those you like best and go there. You will gain much more programming skill through practice in personal projects. This is also voiced by a friend of mine who got her PhD in Germany after doing undergrad in the US at one of the universities in the US listed by OP. But all in all, here are the things that the committee usually spend time on: Your previous research experience, e. I'm currently finishing my PhD in comp neuroscience with a focus on deep learning. io/ You don't need a phd to do machine learning/data science/math, get a basic computer with a good internet connection and familiarity with AWS or rstudio instead. But I don’t know what else to do and really need help. I’ve received a lot of encouragement about continuing my education and also completing my Ph. when i started searching for information about the available jobs in the field and the the job opportunities i found out that most of the jobs requires a masters degree in ML or CS ((jr/entry)NLP positions). I would put engineers who work on machine learning into two broad buckets - Data Scientists - People who understand will understand the data, come up with models to derive insights or train a model to perform specific tasks. 90% or more (I'd even guess 95% or 99%) of research scientists have a PhD. Tasks for data scientists include - I'm doing a PhD in ML and have decided I need a better set-up computationally. Now, I am in industry and I am interested in a wider range of papers because I have to generate interesting ideas. hello i'm a computer engineering student, and i still have 2 years till graduation, i took andrew ng ML introductory course and i was really passionate about the topics. As a member of the ML@GT program at Georgia Tech, I can offer some insights and advice based on my experiences: Embrace the interdisciplinary nature of machine learning: Georgia Tech's ML program encourages interdisciplinary collaboration, allowing you to explore applications of machine learning in various domains such as healthcare, robotics, and finance. PhDs are meant for research positions. Go work for a couple years and if you feel yourself pulled back, then do a PhD. I've been doing research in Machine Learning for over 2 years now. reddit's new API What future can I have with a PhD in machine learning, especially one from CMU compared to other top-tier schools? If you can please let me know, I would greatly appreciate it. I am halfway through the program and have done computer vision, Reinforcement Learning, AI Robotics Car, AI, ML for trading and ML. Depends. One of the younger ones did a machine learning bootcamp after their PhD to transition into ML. You may want to consider looking for programs that use machine learning (ie engineering, analytic sciences, etc) but that aren't explicitly "machine learning" programs, you may both have a better shot and actually land working on projects you're interested in rather than just being I'm a PhD student at a top 3 program and have assisted in the PhD admissions process before (back in 2020, not recently). But that doesn't mean you need much advanced statistics for machine learning. Where else will you get to truly commit to learning in depth about the science and art of machine learning and the enormous opportunity to work on problems that matter. And if you feel like you're being miserable all the time while doing your PhD, you're doing something wrong and need to change something. I have read through some of the comments. I've moved to a senior position, and have been involved with machine learning for my product, but at the end of the day I am mostly building data pipelines and model results delivery for customers, and tools to monitor/manage and support that product. https://sudeepdasari. Am halfway through the interview process with a GV backed deep learning start up for a ML research scientist position and have 3 interviews with Amazon for ML research/ engineering jobs. PhD and long term goals. Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back r/AskAcademia This subreddit is for discussing academic life, and for asking questions directed towards people involved in academia, (both science and humanities). Well, it turns out that in practice, as a small company, you have to spend most of your time doing engineering stuff, and you only get 5-10% of your time to do "real research". I believe you were downvoted because people often use your exact question to insult academics, knowing full well I also have an Amazon software engineering internship this summer, I am praying to God I get a return offer. The PhD is incredibly risky compared to most jobs. _This community will not grant access requests during the I believe that I lack the mathematical maturity for more competitive positions or to go back for a PhD. 19, since I was a young naive high school student Yes most MSTPs and MD-PhD programs at large universities with engineering schools will let you do this. You wouldn't to necessarily need to already know a lot about machine learning prior to starting. It sounds to me like an echo chamber/collective anxiety of PhD students with impostor syndrome. pure math BSc minor in Statistics) and I am in the middle of a PhD in Applied Mathematics. my Bioinformatics practicum and masters thesis might be on creating a tool using machine learning so I will definitely give this a read over the Christmas break once Lastly, practice continuous learning and stay curious. Has anyone trod a similar path? What are some useful resources, books, online courses and already-done projects/examples (preferably by someone in physics) that I can run through myself to get a hands-on, practical experience with ML? For my part, I'm kinda the machine learning guru and she provides insight based on her wealth of experience in traditional modeling in the field so we discuss a lot of how the models are being developed and why I've chosen to do it that way. My understanding is that post-PhD (in ML, or whatever the department is that accommodated your ML research for the bulk of the PhD), the majority of people aim for (1) "research scientist" roles in industry, or (2) focus more on an academic career, or (3) both, simultaneously. What are the differences between Operations Research PhD as opposed to Machine Learning PhD? I've worked on solving operations research problems with machine learning in my undergrad. A subreddit dedicated to learning machine learning Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back r/genetics For discussion of genetics research, ethical and social issues arising from genetics and In Machine Learning labs, funding isn't very likely to run out, so you can take a bit longer and still be funded (at least that was my experience. Because "doing a PhD" means "learning to be a scientist". Machine learning research appears to be supersaturated and it's extremely hard to get noticed. Internet Culture (Viral) Amazing Research scientist is a postdoc in industry, applied scientist is a machine learning engineer with a PhD degree, and data scientist is a data analyst with a PhD degree? Reply reply Top 1% View community ranking In the Top 1% of largest communities on Reddit [D] Growing beyond a deep learning PhD Hi, throwaway because everyone in my lab uses reddit. PhD is about advancing and discovering new things, not about learning about existing things (though that's required too of course, just not the main point). The pay scales are totally different from Europe (exception would be certain European labs of US tech companies, e. I personally didn't start seriously doing research until my 3rd year and was still accepted into a few top programs, one of which was UofT which I'm a Overtime, your mind become good at this process because it learn it was okay to do so. Your interests will likely significantly change over the 5 years of PhD. The only usefulness of getting one is if you are willing to expand the current knowledge of machine learning and AI by inventing new algorithms that can solve a family of problems present in the industry. Wᴇʟᴄᴏᴍᴇ ᴛᴏ ʀ/SGExᴀᴍs – the largest community on reddit discussing education and PhD in Machine Learning/DL is pretty valuable. Political science is the scientific study of politics. You can try OMSCS by Georgia Tech, there is a machine learning specialization: https://omscs. Machine learning is at the most fundamental level statistics. Thank you, guys! /r/Statistics is going dark from June 12-14th as an act of protest against Reddit's treatment of 3rd party app developers. I'm 21 years old and currently pursuing a master's degree in theoretical physics in the UK. Advantages of the US Need master or phd for become machine learning engineer. passing the interviews is Take a look at the CVs of ML/AI CS PhD students at these schools to see what their profile looked like when they were admitted. Also, even if they don't usually do coding in intern interviews, there are other reasons to go off script. I'm considering a career in machine learning and I'm curious about the benefits of doing a PhD. _This community will not grant access requests during If you are comfortable with probability, linear algebra and can squint at calculus then your foundation is sufficient to grasp the basics of Machine Learning. Research is slightly different, but most jobs out there aren't research and you probably don't want one of those jobs unless you've done a phd and love research anyway. Is a master's degree and PhD required to become an ML engineer, or are quality certificates from online platforms like coursea sufficient for a good career? We would like to show you a description here but the site won’t allow us. My ML work is directed at solving actual problems as opposed to pure ML theory. I have been hearing some negativity about PhDs recently, much of it justified I am sure. USF), which One of my undergraduate degrees is in psychology, and after witnessing firsthand the replication crisis that slowly unraveled decades of presumed rigor in the brain and behavior sciences, I If you plan to get a PhD in machine learning and then decide to code general enterprise environment software, it's not going to help. Hello all, Went to McGill University CS from an Ontario high school. in computer science or machine learning. I’m a math phd so mostly school, self study, dissertation. But the key is to figure out if it’s a path they have supported and encouraged others to pursue in the past, or whether This subreddit is temporarily closed in protest of Reddit killing third party apps, see /r/ModCoord and /r/Save3rdPartyApps for more information. My professor/advisor was from the operations research field. 9M subscribers in the MachineLearning community. I. If its not, and you write out what you actually do care about, you may find that < $300k goes so much further than a high COL area. First you need to be clear what you mean by "machine learning engineer". Yes, plenty, but a large % require masters degrees. I think perhaps I will also ask others and maybe the machine learning subreddit to learn more /r/Statistics is going dark from June 12-14th as an act of I don't think OP was asking which technical aspects of machine learning he/she should learn, rather he/she wants to know how to start actually doing machine learning, in practice. And, strangely, despite this significant commitment, the PhD admissions process is often lower-touch than a job interview. On the brighter side though, many universities have started to offer masters programs in Data Science & ML (e. People PAY for a 4 year undergrad degree and now suddenly getting paid to study Its peanuts for what I care about. Econometrics is beginning to incorporate causal machine learning thanks to the work of Susan Athey and PhD is a long time commitment for students and the school (US schools spend about half a million on each CS PhD student) and letters give them insights into what the student's attitude and aptitude are. There are so many successful PhD who have managed to done so without relying on publishing papers only. S. Basically look up “nonparametric statistics” or “high dimensional statistics” in any top 25 statistics departments research and you will see lots of them, while statisticians, are focusing on mathematical aspects of machine learning. If I were a professor (I'm not, I work at a faang), I would only selectively allow phd internships. Right now, as a machine learning engineer but with a strong software developer base, I'm contemplating the idea of switching just to software development, as I see is a sector with better opportunities and better salaries, and right now I'm more into a "I just want to think less, earn more, and have more free time to my things" mood. At $811/module, it is pretty good bang for buck. After all, it's just logistic regression applied at scale. A PhD is also a learning experience and you should be looking for a teacher, not an absent brand name. It's going well and I have a couple of papers published in good venues already. Machine learning ms seems to be in cs departments more and many students that are in those programs do not have that heavy of a math background. Top students have 10+ papers. This is a crucial learning experience. A phd isn't even remotely necessary to use machine learning, and most competent employers know this. Should I choose machine learning (applied to omic analysis) as the topic for my thesis? Here are my pea-brained thoughts about this as a doctoral student who dove headfirst into the ML/AI rabbit hole and now works at a large pharma doing this. If you get that PhD and then decide to work in a My passion for AI and machine learning has grown significantly, and I eventually want to pursue a Ph. I've had 5 PhD interviews in the past year (all are labs in Europe, not in Computer Vision but in Machine learning/Statistics) and they are quite different so I would say it varies a lot from lab to lab. It's a better use of your time to get started getting to know professors and maybe start getting research experience this year than to roll the dice and hope you get into Berkeley next year. I got my Bachelor's degree in CS in 2019 with no real focus on math. Beijing, Tsinghua) for a PhD? Numerical analysis is very important because in the end, the computer has to do the number crunching. Another example, PhDs in China publish crap load of papers each year to demonstrate how smart they are. Applied Mathematics is more suitable considering the amount of calculus that advanced machine learning is going to require. ML is often portrayed as a magical field where anyone with a laptop and Python skills can build amazing AI systems. That is But yeah, it's a crap shoot, especially in machine learning. Not all are first author, but still. However, my applications were turned down, primarily due to the lack of publications in prestigious conferences, which seems to be a crucial criterion for these roles. I have two coding interview rounds scheduled for machine learning software engineer PhD intern at Meta in around 4 weeks. You’ll be better for it (have better ideas of the problems you want to work on, etc). programs in the UK (Oxford, Cambridge, Edinburgh, UCL, Imperial) are prestigious as the top 20 schools in the US. PhD in Quantum Machine Learning I've started by PhD in classical machine learning but a couple of years later switched to QML as I found it interesting. You don't need a PhD to do machine learning work, you don't even need a master's. There are lots of companies doing this, from start-ups to household brands. Others say it's a powerful asset for future jobs and will Pursuing a PhD in machine learning requires a background in mathematics, linear algebra, statistics, and programming, as well as a familiarity with the main concepts and techniques of ML. having said that, it always helps for you to gain some knowledge so that when you apply to programs, they see that you have interest and some knowledge in ML through what you write in your I have done research on machine learning relating to biology and human genetics. In fact, I've seen many PhDs actively shun seriously considering productionization details, partially due to the mindset that some PhD students have (which probably is what drives them to do a PhD in the first place). you will take classes during phd and can master machine learning then. PhD Student in Machine Learning at Mila AMA . reddit's new API changes kill third party apps that offer accessibility features, mod tools, and other features not found in In the UK finding funding is really hard, while in US when you get accepted PhD you are getting funding automatically. Or check it out in the app stores &nbsp; My PhD work is focused on a problem with just those characteristics. The Pressure to Publish: Inside the Mindset of PhD Students in Machine Learning. Here are profiles of people I personally know who are at the top-4 schools (Berkeley, Stanford, MIT, CMU). I have a math background (i. However, the CS department might not be the best place to pursue it. only half or a third of the pay of a full position - but off course full workload). You learn a lot of things during a PhD - like framing your work for the research community and navigating peer review - that’s not helpful for that. 391K subscribers in the learnmachinelearning community. It certainly isn't more than a great introduction though. No one’s mentioned this so i’ll do it: also realize the opportunity cost of doing a PhD is $1M pre-tax, if He has probably picked something that is suited as a first project in a PhD, so it'll be ideal to get your feet wet, see how the infrastructure works, how to collaborate with others (especially your advisor), write your first paper, etc. And I'm working on a comparative study of different machine learning techniques for a paper at the moment. That's the whole point. In fact it is closer to math as a subject than Statistics is. As most of the other commenters said, I think getting used to work with dirty data and a good experience in programming seems to be what matters the most. When I finished my master’s degree, I spoke to them both about pursuing a PhD, the value it would bring me, etc To answer your question: my non-PhD former advisor is a fantastic researcher and he’s well-respected in the field. (New to this so going to sound naive) I'm a senior undergrad student of Electrical Engineering. If what you need is beyond what they can offer, choose smaller experiments - it's not that unusual for PhD work direction to be influenced or restricted by the availability of expensive hardware - like, in physics, if you don't have access to the large hadron collider then you don't do a PhD A place for beginners to ask stupid questions and for experts to help them! /r/Machine learning is a great subreddit, but it is for interesting articles and news related to machine learning. Working on “AI for good” i. I’ve been considering quitting, but I really want a career involving machine learning, statistics, optimization, or even Machine learning learning is a combination of calculus, linear algebra, computer science that usually requires a masters to be proficient at. For the doubters, here’s a random list of PhD students I found by visiting public lab websites. And then he/she said Get the Reddit app Scan this QR code to download the app now. d level normally. Put in about an year into but now I feel absolutely lost, very overwhelmed with the content and I'm unable to find new Following on from the conversation about USA vs UK PhD programs, what are people's thoughts on the top European (e. I will likely get into the program where I'm doing my master's degree, but the group I'm with is doing biomedical NLP specifically and aren't well connected to the NLP field (especially those working on machine learning). Many failed even though they didn't work elsewhere during their PhD studies. Berkeley: Whether it is worth it or not depends on a few things IMO. Letters also indirectly suggest you have a taste of research before jumping into PhD for 5-7 years of your life. This is true. Georgia Tech, UMich, and Columbia are all ~top 10 schools in the US for computer science. What are the best universities in the U. Lots of papers come from Chinese Universities, even smaller ones like Xiamen U, but then again churning out papers en masse isn’t a metric we should value too much. How to search for machine learning phd internships effectively? /r/Statistics is going dark from June 12-14th as an act of protest against Reddit's treatment of 3rd party app developers. reddit's new API changes I am a 29 year old that has been doing software engineering for about 7 years professionally now. Hi, I'm currently a PhD student doing research into machine learning. in that field. 2. If living in a high COL is important to you, cool. I have a PhD in machine learning from an R1 University and 3 years post PhD experience as a professional Data Scientist. Honestly, I think I would have preferred UofT CS but my ultimate decision came down to the drinking age 18 vs. " But you can. Thats my point, cost is relative and interest are too. I have some level of interest in Machine Learning, my Final Year project being based upon it and all. Bioinformatics machine learning PhD . e. Numerical analysis is good for two things: (1) identifying and understanding numerical errors. Currently I'm running most scripts either locally on my Dell XPS 13 (no GPU) or on Colab, but evidently both are insufficient. You need Look at graduating PhD students at MIT, Berkeley, Stanford, and CMU. (2) Learning numerical computation algorithms for solving differential equations, linear systems, differentiation, and integration. However, as someone who has largely enjoyed their PhD in reinforcement learning, I thought I might PhDs are indeed quite competitive, as others have described. Or check it out in the app stores By joining the Expedia Group 2023 Global Summer Internship Program as a Machine Learning Scientist Intern, you’ll fully integrate into our inclusive community and gain a well-rounded experience of life at Expedia Group. It deals with systems of governance and power, and the analysis of political activities, political thought, political behavior, and associated constitutions and laws. besides that, apply to a whole lot of positions until you can get interviews. /r/Statistics is going dark from June 12-14th as an act of protest against Reddit's treatment of 3rd party app developers. As part of my research I developed a model that is getting a lot of interest commercially, and so the university wants to take this application and spin it out into a separate company. 20 votes, 27 comments. Generally speaking, the world of machine learning itself revolves around data pipelines and data pre-processing. It's the traditional route for getting into any Access to such a machine will influence your productivity. focused on ML , and Where I've seen philosophy come in handy is stuff like Robert Miles' youtube channel where philosophy issused to explain the degree to which human paradigms of intelligence, knowledge, and such are inapplicable to artificial intelligence and machine learning; so that we don't make terrible assumptions when dealing with these systems that really give an uncanny sense of An offshoot from the thread discussing Canadian and European unis for ML phds. "You can't do machine learning without understanding the scientific method and, in particular, experimental design. using machine learning for medicine, scientific discovery, ecological conservation, etc. I recently met two people, one of whom is currently pursuing a PhD and another who graduated a year ago. If for whatever reason you feel that you are not doing that in your Phd you need to talk to your advisor and if that doesn’t change things then you have a bad advisor and When I was a PhD student, it was somehow easy to find relevant papers, as I was on a single topic. I've found this incredibly exciting, and freshman year I'd get up at 5, spend most of the day learning about this stuff and implementing my ideas, skip all my classes, and barely manage As. It is very good as an introduction for machine learning or general comp sci PhDs who haven't done deep learning before (I have recommended it to several, and they loved it). Regular PhD students were only offered a 1/2 or even 1/3 post (i. lhegywr wifov vzc aexj kwcncg zaij uwm zbwim nbeqa mtyhgh