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Hi Everyone, I'll be graduated in August 2019 from my engineer degree in computer science. This engineer degree is a study of 3 years in apprenticeship. I did the first year as a software developer, and since september 2017, I continue as an artificial intelligence engineer with IBM. After that, I would like to apply for another master of research degree with a major in computer engineering (Machine Learning). So, I need your advices. Thank you in advance for your help. Regards Zoumana KEITA

I'm interested in pursuing a PhD&career in either data science or machine learning in the future, but don't have an undergrad degree in CS, data science, machine learning, etc. and discovered my interest late so I've only taken 1 intro programming course and I'm assuming they have pretty rigorous pre-reqs I majored in Physics at Cornell (cum.~3.6, 3.3 in major) and have had a couple years of research experience in multiple settings (working on first publication). I'd be willing to take a masters program in between, but wondered what my best option would be to make the transition given my relatively limited background.

So here's the dilemma: I'm a pure math guy who's mostly interested in theoretical computer science, but also partly interested in theoretical machine learning. I've been accepted with similar funding yada yada to both UCLA and UC Irvine's CS PhD programs. The professor at UC Irvine more so matches my interests (graph theory, approximation algorithms, pure math-y stuff) and he's also highly distinguished, coming off a 15 year stint doing theory at Georgia Tech. The professor at UCLA is more engineering focused. There's a few questions I need answers to influencing my decision: How important is the prestige of a university compared to the prestige of one's research advisor when it comes to securing faculty positions? How easy is it at UCLA (or in general) to either co-advise with another department (theory) or switch entirely, and would this affect funding offers? Any advice would be greatly appreciated!

Hi all, I am an international student at Purdue majoring in computer engineering and I am now currently a freshman. I now have a GPA of 4, which I know is not impressive since I am a freshman, but I am getting ahead of the usual schedule, so there should be more time for me to do some research stuff in sophomore, junior, and senior years than usual students. So I am doing research this summer in Beijing, China, with a well-known Chinese professor whose h-index is more than 67 (lowered because he has two accounts in Google Scholar with the other one 54) in, hopefully, machine learning from May to August, 2018. By hopefully I mean he told me to prepare the knowledge of machine learning and a new structure of machine learning (I am not going to say which is that), but he also said something I am not sure whether is related to machine learning or not. Also, there is a professor in the ECE here at Purdue whose research interest is machine learning. I plan to use my 2018 summer research experience to ask him for a research position in his team.what So assuming I have a shot at the top programs, what do I need to do? When do I take the GRE test? Is there a difference between a GPA of 3.9 or 4.0?

What are the main research focuses of the Yale Statistics Department? For someone interested in machine learning, statistical genetics, and computational methods, would it be a good fit? Any general pros/cons about the department? I know it's a small department, so wondering how its size sets its apart from other institutions (in both good or bad ways).

Could you kindly evaluate my profile and let me know about my chances for admission at the below-mentioned universities? Thanks. I want to pursue PhD in computer science, specifically in fields related to computer vision and image processing. List of schools planning for: MIT, CMU, Oxford, Cambridge, UIUC, UCF, UMD, UMass, SUNY, U Toronto, Georgia Tech, U Texas Austin, Caltech, EPFL. Credentials: GRE: 331/340, AWA: 4.0/6 GPA: Masters (M.S) with 3.85/4 at UC Santa Barbara. Masters (M.tech) from IIT (India) with cgpa: 9.01/10. Bachelor's CGPA: 8.82/10 3 publications: 2 journals in Elsevier (I.F: 2.52 and 2.10) and 1 second-tier conference 1 conference paper in preparation for submission. (All publications as the first author in computer vision related topics) Recommendations - 2 from UC Santa Barbara and 1 from IIT. Pre-application replies from: UCF, Oxford, Cambridge, EPFL, UIUC, SUNY, U Toronto Work experience: 1.5 years as software engineer at Samsung Research Institute.

Hello, I am a sophomore/junior at Pennsylvania State University in Computer Engineering. I plan to graduate in three years and I am conducting undergrad research. My cumulative GPA is 3.74/4.00. I want to attend grad school at a top 10-25 program in Machine Learning if possible. I wanted some advice on things I could do that could make me stand out and if shooting for top 10-25 programs is practical. Thanks

Hello all, I have applied to UBC, UofT, UAlberta, UOttawa, Queen's, Mcmaster for MS in CS programs. I am hoping to get admits from UBC and/or UofT. Here is my profile. I have no publications. Strong academics (8.65) from a good college from Gujarat,India. IELTS 8.0 bands. No GRE. 3 internship stints and 2 technical training stints. (2 of the internships were on Java and Cloud/ML) Strong leadership skills and extra-curricular activities as well. I have formed a strong SOP mentioning my clear vision and broad interests in the field of AI, ML , DL and CLoud computing , along with a few professors and their research groups. Any one admitted from UBC of UofT MscAC program if available to comment on my chances , Please do So! Also, I have heard that UBC and UofT conduct interviews before giving acceptance offers. What is asked in those interviews? If anyone experienced is available, please throw some light on the matter and please rate my chances if possible. Thankyou. Sagar Parikh Resume.pdf

Hi all, It has been quite the journey for me to post on this forum. I tried creating an account multiple times but got some type of error before successfully creating it using a gmail account. Anyway, I'm from a senior year undergraduate from Nanyang Tech in Singapore and I'm considering a PhD in the US (in machine learning). As the deadlines approach in a couple months, I can't help but wonder if I have chosen the right universities to apply to. Some background about me: 3.54/4.00 cGPA (approx.) I was on the accelerated program, completing a 4 years bachelor degree in 3.5 years Specialisation in intelligent systems and data science (machine learning, data science, intelligent agents, those sort) about 1 year ++ research experience in machine learning due to my senior thesis submitted a first author paper of said research to a conference recently (but let's not assume it'll get accepted) working on a short 2 month long research project with a prof in the US (via Skype and stuff - his alma mater includes Boston U and MIT, though I'm sure he's been out of academia for some time) Started my own business in my second year of college Have an internship with IBM on software developing, but could potentially work on some machine learning related stuff GREs: not yet taken, but I am confident of scoring well TOEFL: some universities may not require this from Singaporean students but I'm also confident of scoring well (since English is my native language) LORs: 2 strong/relatively strong letters plus 1 from a prof who asked me to draft the letter so he could sign (I guess its up to me to make it a somewhat strong letter) I don't think my GPA is great, but I was on the dean's list for my final year of studies. For what it's worth, some of my uni's undergraduate level courses are taken by masters students so maybe it is somewhat equivalent to a graduate level course in the US. These are the universities I have currently narrowed out (in order of preference): Columbia University NYU UCLA UPenn Boston U Stony Brook CUNY Graduate Center If anyone has any suggestions on which universities I should add or remove from the list or just anything in general, feel free to comment! Grad school admissions aren't exactly straightforward so I'm having a little trouble deciding on which universities I should apply to. Oh and I should mention, I was offered a PhD position in my current uni but it isn't exactly my first choice. --------------------------------------------- On another note, I am concerned that my nationality might be a problem, especially after Trump took office. I am originally from Malaysia, but immigrated to Singapore as a baby. I've had a permanent residency status in Singapore for my entire life, but never converted to a citizen so my nationality is Malaysian. As you may or may not know, Malaysia is a largely muslim country. I didn't think this was a problem until a bunch of my friends mentioned it. Will this pose as a problem for grad school applications?

Undergrad Institution: Large State School Major(s): Mathematics Minor(s): Statistics GPA: 3.95/4.00 Type of Student: Domestic White Male Grad Institution: Same Large State School Concentration: Mathematical Statistics (Masters) GPA: 4.00/4.00 GRE General Test: Q: 167 (92%) V: 165 (96%) W: 4.0 (60%) Programs Applying: Statistics PhD Research Experience: 1 Peer-reviewed publication, 2 summers of research as an undergrad, presented at local/state conferences for undergraduates (won some minor awards), Graduate Research Assistant for one year, which I assume will lead to a publication Awards/Honors/Recognitions: Phi Kappa Phi Outstanding Scholar, wrote grants which resulted in $5000 in funding for my research, Goldwater Scholarship Nominee, Honors College "Excellence in Research" Award Pertinent Activities or Jobs: Math tutor when I was an undergrad, Teaching Assistant for one semester, Research Assistant for three semesters in grad school Letters of Recommendation: Two from professors I have done research with (one young in her career, one a bit older with a good reputation), one from either my department head (who is on my committee) or from another prof on my committee with a good reputation Any Miscellaneous Points that Might Help: my masters degree (and the qualifying exams I have taken) are the first part of the PhD program at my institution, so I have proven myself in some advanced courses (Measure Theory, Computational Statistics, Asymptotic Statistics) Programs considering: Personally, I am from the middle of no where in the midwest, and I would ideally like to move to a location with better weather, more interesting terrain, and/or a good music/art scene. Academically, I am interested in machine learning/computational statistics, and, in particular, like working with text data. On the side, I am also interested in data visualization. Academia could be in my future, but I would like my program to have good industry ties. If possible, I would prefer a small-medium department to a large one. Very interested: New York University (NYU) (Data Science PhD) (My favorite program that I have seen. I like the research areas and industry ties, are there other programs like this I could apply to?) University of California, Los Angeles (UCLA) University of Texas at Austin (UT Austin) Interested: University of Washington Columbia University Iowa State University (Visualization group is very interesting) University of California, Irvine Also Interested but, I haven't done a lot of research into these departments University of Chicago Duke University Rice University University of Michigan Do you think I am competitive at these programs? I would like to remove some of these schools from my list and replace them with some safer schools and ultimately end up with 5-7 to apply to. These are also all fairly larges departments, and I think I am much more suited to a smaller one. Any suggestions are welcome.

Hi! Please I need help trying to figure out if I have chances to get admitted to some MS CS Programs. Here is my profile: Research Focus; Machine learning (Deep learning, reinforcement learning, unsupervised learning) Final Objective: PhD from a US university. Looking to work as a professor or a research scientist in industry after graduation. Undergraduate: BS in EE from an ABET accredited university in South America (TOP 500 worldwide, best in my country) Position in class: Class topper Research experience: Small projects in signal processing, controls and VLSI. No publications. Work experience: Unrelated to the field I intend applying to, I was a TA at my home university though (math, physics and EE courses). Motivation: I got motivated by small exposure to AI from signal processing and controls graduate level courses that i took as a free student, and also from online MOOCs. GRE (310): V149, Q161, AWA 3.5 TOEFL (100): R27,L27,W24,S22 My plan: Since I consider that my profile is not competitive enough for top/middle tier PhD programs, I am planning to apply to MS programs first and then jump to a more prestigious one after graduation. Here is the list of universities I've come up with, which offer good coursework and research opportunities related to my interests. 1 CMU 2 Stanford 3 UT Austin 4 U Mass 5 NYU 6 Columbia 7 Purdue 8 UC Irvine 9 U Michigan 10 USC 11 Northeastern U Please guys, be honest with me about which universities should I take off of my list and which ones should I add. Thanks !

Hello I'm planning to apply for Ph.D. in Machine Learning(Fall 2018). I've been researching a lot about various colleges where I would want to apply. I have made a list of places according to the area of my interest and the research that's currently going on. I would be really grateful if you can help me in selecting from the lot on the basis of the value of the place and the availability of funding. These are the two most important filters I want to apply on my list. A number of 10-15 universities would be helpful. PS: I understand that selection of a college is based on other individualistic parameters as well, which I will use. But, please provide your valuable insights based on the two points mentioned above. ---------------------------------------------------------------------------------------------------------------------------------------------------------- University of Bristol TU Dortmund U Penn University of Maryland Arizona State University TU Darmstadt Cambridge Purdue Columbia University FU Berlin Georgia Tech Aalto University John Hopkins HU Berlin Brown University of Waterloo Cornell University University Wuerzburg University of Wisconsin-Madison University of British Columbia University of Toronto University of Bonn University of Montreal University of Amsterdam University of Alberta University of Stuttgart University college of London University of Surrey UT Austin University of Munich University of Michigan University of Southhampton Saarland University University of Waikato Universiy of Washington Unversity of Nottingham Technical University of Munich UC Berkely University of Illinoi University of Exeter University of Helsinki ----------------------------------------------------------------------------------------------------------------------------------------------------------

Cross posting it from the Computer Science thread: I've got admits from University of Colorado, Boulder and University of California, Davis, both for MS CS programs. I'm interested in machine learning (and NLP, recently) and I'll probably not be pursuing a PhD after MS. From what I've found, UCD is somewhat cheaper overall and has marginally better ranking in ML but is pretty poor NLP wise whereas CU Boulder is great in NLP. Research interests line up in both schools. No funding in either, and as far as I'm aware, job opportunities are somewhat better in California than Colorado because of the tech scene. My main issue is whether a degree from UC Davis is perceived as being of "lesser" value because it has a reputation for being rather laidback? And does the UC tag by itself hold any value over CU Boulder? Do you guys have any suggestions or any other information that could help?

I've got admits from University of Colorado, Boulder and University of California, Davis, both for MS CS programs. I'm interested in machine learning (and NLP, recently) and I'll probably not be pursuing a PhD after MS. From what I've found, UCD is somewhat cheaper overall and has marginally better ranking in ML but is pretty poor NLP wise whereas CU Boulder is great in NLP. Research interests line up in both schools. No funding in either, and as far as I'm aware, job opportunities are somewhat better in California than Colorado because of the tech scene. My main issue is whether a degree from UC Davis is perceived as being of "lesser" value because it has a reputation for being rather laidback? And does the UC tag by itself hold any value over CU Boulder? Do you guys have any suggestions or any other information that could help?

Hi. I'm a CS student (Bachelor's + Master's), who got accepted to Princeton for a CS PhD (Machine learning). I'd applied to a ton of other places (ten in total), but got rejected from all of them (apart from an MS in Data Science at CMU which I rejected for various reasons). Now, I wanted to defer the PhD for a year, since having been in academia for pretty much all my life, I think it might make sense to see how the tech functions in the "real world": a perspective I don't think I gained enough during my internships or undergrad. I also wanted to do this because I'm a little tired of academia at this point, and a long term goal of mine is to create real world impact: which might be easier in startups/industry than by doing theoretical machine learning in the academy. Unfortunately, I recently found out that Princeton CS does not allow deferrals, so I'd have to reapply next year with the risk of not getting accepted. A prof also mentioned that there's no guarantee that I'd get accepted again if I were to apply next year. However, the graduate coordinator told me that 1-2 students do this every year, and said they understand my reasons for wanting to take a break. She also mentioned that of the three'd who declined to reapply in the past three years, there was only one who reapplied (and was successful). So there isn't really enough data to assign any good probability value to my chances next year. Given all of this, do you guys think it makes sense to grab what I have and try getting practical experience via internships (of which there would be a good number for an ML student) over the next two years, and then figure if I want to continue in academia on the way? The problems the Princeton guys work on are definitely very exciting to me, but I'm not sure I want to spend the next five years in academia at this stage in life. It's pertinent to note that ML PhDs seem to be very competitive (ten rejects hurt the ego more than I thought they would). Also, my profile might not improve terribly during the next cycle: right now, there are no publications, but there are a bunch of projects, courses and academic awards, and a Master's thesis is in the offing. Thanks, and do hit me up for more clarifications!

I have been admitted to the Masters program at Virgina Tech, Blacksburg Campus. I have completed my undergraduation in electrical engineering. My work experience lies in the automotive industry. My area of focus is in Software and Machine Intelligence or Signals and Systems. I could choose between these two. I intend to study subjects concerning Machine Learning and work towards a thesis in autonomous systems. Since, I do not have any substantial coding experience, just self-taught Python etc., I would like suggestions on choosing the coursework for the graduate program. I plan to have the following courses to satisfy the program requirements - Pattern Recognition Convex Optimization Deep Learning Advanced Machine Learning Probabilistic Graphical Models and Structured Predictions Advanced Topics in Intelligent Systems Theory of Algorithms I plan to learn Data Structures, Java, Python and build up a strong mathematical background in the next three months, before graduate school starts. I also intend to work in a lab, to perform research for my thesis or towards a project. I am afraid that my coursework is quite heavy, though those fears may be unfounded. Any advice on choosing the coursework for a non CS background guy would be appreciated. Any leads on preparing for this courses would also be appreciated! Thanks!

I have been admitted to the Masters program at Virgina Tech, Blacksburg Campus. I have completed my undergraduation in electrical engineering. My work experience lies in the automotive industry. My area of focus is in Software and Machine Intelligence or Signals and Systems. I could choose between these two. I intend to study subjects concerning Machine Learning and work towards a thesis in autonomous systems. Since, I do not have any substantial coding experience, just self-taught Python etc., I would like suggestions on choosing the coursework for the graduate program. I plan to have the following courses to satisfy the program requirements - Pattern Recognition Convex Optimization Deep Learning Advanced Machine Learning Probabilistic Graphical Models and Structured Predictions Advanced Topics in Intelligent Systems Theory of Algorithms I plan to learn Data Structures, Java, Python and build up a strong mathematical background in the next three months, before graduate school starts. I also intend to work in a lab, to perform research for my thesis or towards a project. I am afraid that my coursework is quite heavy, though those fears may be unfounded. Any advice on choosing the coursework for a non CS background guy would be appreciated. Any leads on preparing for this courses would also be appreciated! Thanks! ECE5524, Pattern Recognition

Hi I am CSE major interested in machine learning. I graduated from university in asia in 2016(undergrad rank 40 in world), and worked in a startup as CTO(co-founder). about a year of working out, I found that I really want to study more about machine learning. Can you guys give me some advice on my current status? I have no information what school I'm eligible for. overall GPA (3.5/4.0) but GPA after 2 years of military service is 3.8/4.0 (which consists large portion on my undergrad credits, don't know this can appeal, but I worked hard to follow up after discontinuity of studying career. I know this GPA is not that high though. ) TOEFL 110 2 machine learning related paper (not published / undergrad level paper) : 1 computer vision related, 1 bio-informatics about 1 years of Industry experience (1 project with machine learning company : voice recognition) : didn't do research about models but I worked as leading developer. but it gave me insights about A.I. industry, how ML pipeline is managed etc. and planning for an internship at Vision or NLP labs for research experience about 2 year from now on. Hoping my paper get accepted. Though I currently don't have any good papers, I have strong interest in boosting multiple models and GAN related ideas. hoping I can expand these thoughts and experiment on them. I'm thinking maximum 2 years research internship until I get accepted at any CS PhD programs in US. After Discussing with my school friends, I know my application is not that good but willing to fill the holes. Please give me some advice on what kind of program I'm eligible for. Thank you in advance

Hello, I'm currently studying Mathematics and Econometrics, and I am very interested in pursuing a PhD and research career in the mathematical aspects of data science (Artificial Intelligence/Machine Learning). I have a decent amount of coding experience in R and Python, but I have not taken any CS classes. Is Operations Research a discipline that would allow me to pursue my research goals? Are there any other types of programs that I should look into? I am currently applying to OR programs, and NYU's Data Science PhD program. Any help you can provide would be a huge help. Thanks!

Here are my stats: Undergrad Institution: University of Colorado, Boulder Major: Mathematics, Computer Science GPA: 3.81 Type of Student: White Male Upper Division Courses: Math: Calc III (A-), Analysis I (A), Analysis II (A), Intro Linear Algebra (A), ODE (A), PDE (A), Operations Research (A), Numerical Analysis I (A), Probability Theory (A), Statistical Theory (A), Markov, Queues, Monte Carlo Simulation (A) Next Semester: Stochastic Processes (Grad Level) CS: Algorithms (A-), Artificial Intelligence (A), Machine Learning (A) Others: Generally A- and As in the basic maths and computer science courses. Two Bs (one in Calc II), several B+ (all non-related classes), a B- in a history class. GRE: 168Q, 165V, 4 Research Experience: year long research project with two professors in math department, undergraduate thesis in machine learning area Awards/Honors/Recognitions: Dean's List, several (recent) scholarships Pertinent Activities or Jobs: BB bank quant summer analyst, Data Science team, active with elementary/middle school volunteer programs Letters of Recommendation: -Two mathematics professors (research with both) -Computer science professors (thesis advisor) Plan to apply to (mix of MS/Phd Programs): Chicago, Berkeley, Harvard, MIT (CS only), Penn, Yale, CMU, Wisconsin, Northwestern, Toronto, Cornell, Texas (CS only), UCLA, Virginia, and a few "safes"... Am I reaching too much for some of these schools? I would rather not settle since I have an offer from the same BB bank I interned at, but I'd also prefer to further my education. Would working a year or two help at all?

Hello, I'm currently studying Mathematics and Econometrics, and I am very interested in pursuing a PhD and research career in the mathematical aspects of data science (Artificial Intelligence/Machine Learning). I have a decent amount of coding experience in R and Python, but I have not taken any CS classes. Is Operations Research a discipline that would allow me to pursue my research goals? Are there any other types of programs that I should look into? I am currently applying to OR programs, and NYU's Data Science PhD program. Any help you can provide would be a huge help. Thanks!

Background- The Good: - The 3 yrs BA experience (working in the analytics field has actually inspired me to go one level deeper and transition into ML) The Bad: GRE: 319 (V:155, Q:164, AWA:4.0) TOEFL: 107 CGPA: 3.20 The Ugly: - No publications - Undergrad degree in Mechanical engineering I have completed Andrew Ng's ML course on Coursera and I have the below mentioned tasks in mind before I start my applications for Fall '17 Kaggle - Pick any of the ongoing/completed competitions and showcase the methodology/performance comparison between models on any blog/github Replicate results from research papers - I have found some really cool research papers and I really want to replicate their results with some modifications. But unsure if it would be worth the effort/time and if it would even help my case Also, in parallel to these things I am planning to complete some more online ML courses to try and get a more comprehensive understanding. Does this seem like a good idea OR is there something else that I should be doing? Do I even have a chance? Should I retake GRE? Should I give up looking out for grad admissions and go hunt for jobs directly? I know this is a bit late wrt to the fall applications this year. But at this point I'm getting all out paranoid. Appreciate the help!

Hi all, I'm just curious to see if anyone out there is interested in, or already active within, the new subfield of computational psychiatry. I have not come across a thread where it is mentioned, so I figured I would ask! There is actually a new journal for this specific topic, for those who may not know: http://computationalpsychiatry.org

** Sorry, I had posted earlier, but I forgot to mention a few things. ** I just graduated with two bachelor's degrees in biology and psychology. But, I want to go to graduate school to study data science or machine learning. I started taking pre-reqs for ML a year and a half ago. (Discrete structures, multivariate calculus, linear algebra, probability theory, intro to programming, OO programing/data structures) I have a 3.955 undergrad GPA. ... I'm currently set to enter a CS post-bac certificate program for fall 2016-spring 2017. This program will allow me to get the rest of my CS pre-reqs covered. (For example, I'm taking advanced algorithms, software engineering, computer systems, and data science in the fall.) ... And I've done a year of neuroscience research, including an undergrad thesis. I have one paper submitted, second author that should be published by the time I apply. But, it is NOT computational neuroscience research. ... Additionally, I'll have good/great recommendations from my research PI, computer science professor, and my supervisor at my boss. (I'm like an undergrad teaching assistant for organic chemistry.) I think that the biggest obstacle for me will be the fact that I have not done any CS research, and I won't have time to do any before I apply for a masters program. Do you think it's feasible for me to get into a top 10 machine learning grad school (Stanford, Carnegie Mellon, Berkeley, etc.) even though I have no CS research experience? Do you think that the fact that I've done neuroscience research will be enough to show that I have research potential?

I just graduated with two bachelor's degrees in biology and psychology. But, I want to go to graduate school to study data science or machine learning. I started taking pre-reqs for ML a year and a half ago. (Discrete mathematics, multivariate calculus, linear algebra, probability theory) I have all A's currently, and I had a 3.955 undergrad GPA. (But I don't know if that matters.) I think that the biggest obstacle for me will be the fact that I have not done any CS research, and I won't have time to do any before I apply for a masters program. But, I have done a year of neuroscience research, wrote an undergad thesis, presented a poster, and I have a paper submitted. Do you think it's feasible for me to get into a top 10 machine learning grad school (Stanford, Carnegie Melon, MIT, Berkeley, etc.) even though I have no CS research experience? Do you think that the fact that I've done neuroscience research will be enough to show that I have research potential?