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Found 8 results

  1. I looked through the profiles of PhD students at top 4 ML PhD programs (BAIR at Berkeley, SAIL at Stanford, CSAIL at MIT and CMU ML at CMU) and it seems PhD students in these programs are mostly from Berkeley, Stanford, MIT, CMU, Harvard, Princeton, Tsinghua or IITs. Is it just because students from these schools are just smart and hard-working or is it because undergrad/masters prestige matters in ML PhD admissions? I have been admitted to Georgia Tech for Spring 2023 as a transfer and I am going to major in math(statistics concentration) and CS there. But after looking through the profiles of the ML PhD students at these programs, I am starting to wonder if I have to stay one and a half year more at my current community college in California and try to transfer to Berkeley EECS instead of transferring out asap from community college... Do you think it's a good idea for me to sacrifice up to one and a half year to get into Berkeley EECS or should I just transfer asap to GT (if I am interested in pursuing ML PhD)? I also got into Columbia GS btw Many people advise me to get out of community college as soon as possible since there aren't much things I can do in CC, but after looking through the profile and finding only one GT graduate in CMU ML PhD program, I am starting to wonder if I really need to try to transfer to Berkeley.. Should I just transfer asap and go for masters if I get rejected from top ML PhD programs? (Like apply to PhD programs for Berkeley and MIT but for Stanford apply to masters and for CMU, apply to both masters and PhD + MSCS at Princeton and Cornell)
  2. Hi, I am looking to do my PhD in computer science, with a focus on AI and machine learning.I am lucky to get two fully funded PhD offer from Purdue CS and Penn state IST (information Science and Technology). Offered an RA ship in Penn state. The professor is pretty good. Offered TA ship from Purdue. Students usually have 2 years of time to explore before settling down with a professor. Your suggestions are highly appreciated.
  3. I have always been interested in my pursuing Machine Learning when I was in college. However, I was really disappointed in the quality of teachers in my undergraduate college. I grew disillusioned in what was being taught and decided to pursue my own interests in college; I researched topics by myself, made projects, learned stuff from the internet. Eventually, I was able to secure a pretty good internship as well because of my efforts. But my academics did suffer because I invested my efforts elsewhere. Now I want to apply for a master's in the specialization for ML. If possible, I will also like to pursue research opportunities in this field. No matter how passionate I describe myself in my SOP, the admissions committee is going to be skeptical of my commitment because of my low grades. I may be wrong about this, but I think it will improve my chances if I acknowledge my poor academics in my SOP, but I don't want it to overshadow my accomplishments or the hard work I put in to follow my interests. People who have faced this problem before ( or actually, anyone who has experience with these things) what would be the best way to address it in my SOP? How should I phrase/word it? Any examples are of course appreciated. Anything I should be careful of/steer clear of? Thanks
  4. College: IIT ( ranked among top 3 in India ) GPA: 7.8/10 ( 3.12 on a scale of 4 ) BSc in Maths ( 2021 graduate ) Minors in Machine Learning: Courses - Data structure algorithms (B grade), Intro to Machine Learning ( A grade ), Bayesian ML (A grade) Minors GPA: 9.2/10 (3.68 on a scale of 4) GPA in last three semesters (out of 8 sems) : 9.3 (3.72), 9.5 (3.8), 9.2 (3.68) I am confident that I can get a good score in GRE and TOEFL which I will give this year. I also have perfect grades in CS and Economics elective courses ( Database systems, Cryptology, Financial economics, Intro to economics) My GPA is low mostly because of compulsory abstract math courses which I hated and few stats courses offered by my maths department I don't have any publications but I have done some research projects in my courses related to machine learning. I will be starting my job as a data scientist in a startup in the next few weeks but I want to pursue higher studies abroad. What different programmes can I consider in the US and outside US (Canada, Europe, Singapore, Japan)? I don't think I have any chance in any of the top 25 universities. I am searching for Data Science programmes or CS or Statistics programmes with the freedom to choose core ML electives and good career opportunities. Will my good GPA in the last few semesters or minors in ML give me an advantage in some universities? Also, none of my professors knows me personally but I might be able to get one academic LoR and one recommendation from my corporate internship.
  5. The most exciting branch of Artificial Intelligence, Machine Learning is all around us in this modern era. As Facebook suggesting the stories in your feed, same Machine Learning brings out the power of data in a new way. It works on the phenomenon of working on the development of computer programs that can access data and perform tasks automatically through predictions and detection, Machine Learning enables computer systems to learn and improve from experience continuously. What is Machine Learning If talking about Machine Learning definition then, it is a core sub-area of Artificial Intelligence (AI). Machine Learning applications learn from experience (well data) like humans without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves. In other words, with Machine Learning, computers find insightful information without being told where to look. Instead, they do this by leveraging algorithms that learn from data in an iterative process. While the concept of Machine Learning has been around for a very long time (think of the WWII Enigma Machine), the ability to automate the application of complex mathematical calculations to Big Data has been gaining momentum over the past several years. At a high level, ML is the ability to adapt to new data independently and through iterations. Basically, applications learn from previous computations and transactions and use “pattern recognition” to produce reliable and informed results.
  6. Hi everyone, I am a student of mathematics and computer science interested in optimization, theoretical computer science, and machine learning. I would really appreciate some of your thoughts on 2 PhD programs I am deciding between -- ECE at UT Austin and CS at Yale. I am an international applicant and ideally wanted to visit both of these (very different) institutes before taking a call, but that just isn't possible now. Thoughts on UT Austin Strong group in ECE and CS departments. Will I be able to work with faculty in CS? Will the large size of the institute prohibit me from doing so? Austin seems like a cool city, but is somewhat isolated. Will I have to catch a flight to go anywhere? Too many undergrads. May face funding issues. Growing safety concerns. The size of the university probably allows for greater opportunities. I think I'll find like-minded people no matter if I want to start a company, enter industry, or stay in academia. Not from an ECE background, so not sure how that will pan out. Reputed within CS folks. ECE probably won't let me indulge in theoretical CS. Thoughts on Yale Small(er) school. This is both a pro and a con. Not sure if I'll be able to find enough people with similar interests at Yale. However, the size will allow for more inter-departmental discussion? Growing CS dept. (but fairly average at the moment) Smacked between Boston and NYC. Strong math department. Funding situation should be good. Mostly with an undergrad focus. Do grad students feel neglected? New Haven safety issues. Reputed in general. Thanks in advance everyone!
  7. Hi guys, new here and didn't see many posts about imaging informatics/radiomics (like applying Machine Learning and Deep Learning to biomedical images for automatic disease detection and uncovering biomarkers for elucidating pathologies).. So I wanna start this thread and seek some advice about my choice of programs (I know it's kind of late but I just know there is this gradcafe forum 😭). Thanks guys!!! Undergrad Institution: Top5 in China Major: Chemistry (B.S.) Overall GPA: 3.32 / 4.00 Grad Institution: Columbia Univ. Major: Biomedical Engineering (M.S.) Overall GPA: 4.0 / 4.0 Demographics/Background: International Student GRE Scores: 321 (V152+Q169+3) TOEFL Scores: 116 (But can be waived for most schools as having a MS in the US) Research Experience: 1. Undergrad Summer (2017): 2mo. Microscopic image deconvolution. 2. Undergrad internship at a start-up (2018): 5mo. Developed computer-aided system. Machine Learning in classification of low-grade glioma and prognosis prediction. structural MRI images. 3. Grad Summer (2019, starting from May): 2.5mo in the summer and still working on it. Deep Learning in age prediction. structural MRIs, functional MRIs. 4. Grad (now): Starting from Sep (around 2mo). Deep Learning in classification of schizophrenic patients. PI says I could have a first-author pub (but it by no means could be submitted before Jan or Feb 2020) 5*: Course Project: 2mo. This is not like a real research but a project for "deep learning" course. Electronic Microscopic image segmentation. LOR: 1 instructor from Machine Learning course (I am the TA this semester and did a project for this course at that time) at grad school, received A. 1 from 3 above and 1from 4 above (should be both strong... not sure if very strong, but at least strong). Publications/Abstracts/Presentations: Basically no (I don't think 4 above counts). Have presented 1 above in a technology forum at undergrad school. During the this forum, have done poster and oral pre (got "excellent oral presentation" award) and my reports was collected in the proceedings of this forum (but this forum is a very very small one run by the Chemistry department... 😭) No pub. But I have written journal-style reports for almost every project that I have completed. I got a website and these reports and other info can be viewd on it. Awards/Honors/Recognitions: Like some outstanding student scholarship etc. in undergrad. Grad no. But in Grad School (May 2019), participated in a Health Challenge and was in the 3rd place (very very very small competition... I think no one will have any idea about it). Fellowships/Funding: NO (International students cannot apply for like NSF....etc) Pertinent Activities or Jobs: TA as described in LOR section. Anything else in your application that might matter (faculty connections, etc.): I think I have illustrated my passion and drive etc. well in my SoP (basically a good SoP). Also emailed profs to ask if they have positions for PhD. 1 interview from UPenn/Bioeng. 2 "encourage to apply" from Stanford/EE, Yale/BME. 1 casual talk from Vanderbilt/CS (not very academic) Research Interests: Biomedical imaging, image analysis, machine learning, deep learning, biomarker, radiomics, imaging informatics. Institutions/Programs: Columbia / BME Yale / BME UPenn / BE Emory-GaTech / BME Uwashington / BE WUSTL / Imaging Science JHU / BME Vanderbilt / CS Stanford / EE Comments: These schools are like all top schools and I am therefore very concerned I would be rejected for all. I guess I could phrase my questions like 3 points Are these schools too high for me? Should I change or add some safe schools? My interest is not related to biology, like cell culture, tissue engineering etc.. It's more like about coding and image analysis. I heard Stanford EE is like highly highly highly competitive. But there is a EE professor just answered my email and he looked like highly encouraged me to apply (but he is not in the committee so he doesn't have a say in admission). I was so like not sure if I should apply... Any other suggestions considering this is extremely late (some schools are due on Dec 1st). Many thanks guys!! 😭 Shane
  8. Undergrad Institution: My own state university in Computer Science Engineering GPA: 3.3 out of 4.0 scale Type of Student: International Male GRE General Test: Q: 162 V: 142AWA: 3.0 Programs Applying: MS - Applied mathematics and statistics Research Experience: 1. Undergrad final year project on unsupervised machine learning 2. Projects on XRD data prediction using supervised model 3. Projects on time series data prediction with neural network model Publications: No research paper published yet. I am interested in continuing research on statistical machine learning, hence opting for applied math as graduate major. Help me with suggesting universities I could apply for.
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