No surprise considering my assignment score. Spring 2020 version Tests were open book, one week long, with many of the subjects being completely new exercises. are very foreign concept to you consider going through. If you dont know the material already, dont even bother. With practically everyone drawing from the same book, youll keep running into the same examples over and over again but sometimes having it explained by another person just makes the idea click. This (the breadth of topics covered and the details you must know on the topics) makes it difficult to know where to ask questions ahead of time as using most outside course material is not allowed. I found this course to be extraordinarily challenging but also very well run and informative. One of the best courses out there. The algorithm was neither clear nor straight forward, and there wasnt a lot of instruction or guidance to make it straight. Theres a lot to say about this course. Projects are all auto graded, which is nice. This is certainly an opportunity to become familiar with the AI field with an intensive, hands-on experience from which you will benefit greatly. Many dropped out around the first midterm, and the remaining students were rather remarkable as a whole. I enjoy that fleeting feeling. Id implemented decision trees in ML4T though others who didnt have that experience also seemed to find it a lighter project. Search (33 hours) - This one isnt too bad if you taken GA or done graph problems on leetcode before. And one needs all the time possible for assignments. Overall, a challenging but rewarding class! There is almost no value to this class outside of them. There is just so much material that the semester feels way too rushed. Some lectures seem to be re-used Peter Norvig and Sebastian Thrun videos that are nearly unwatchable and not remotely on par with other OMSCS classes. Thoughts: Very interesting but challenging class. I expected this course to be pretty tough and time consuming based on previous reviews, but I never spent more than 15 hours a week on this class. Overall, I loved this course, loved the knowledge it gave me, and it definitely made me a better overall engineer. If thats you, GT thanks you for your donation, see you next semester when you withdraw and try again. It probably taught me more than any homework. Definitely read the chapter 13 & 14, probability and bayes net (BN Representation) before semester begins. The final exam was medium difficulty and midterm was easy. See: XGBoost and LightGBM. Lastly, keep in mind that 1,000s of people have passed this course before you and 1,000s will pass after you. The 6 assignments/projects are due biweekly, and you have to spend a lot of time on it. Ill proceed by briefly listing pros and cons. One of the hardest, challenging, and time consuming classes I have ever taken and I loved every minute of it. Students only posted on assignment-related threads. The other class members (and some TAs) were quite accomodating on Piazza. I wish there was less focus on the math and more on the ideas which is what I thought a survey class would do. Heavy workload, but no pain no gain. -Piazza is closed and any questions will be immediately removed. Im not saying dont take this class because of the exam, but it was the last thing we did in class and it left an extremely bad taste. submission.py: Where you will ultimately put your probabilities and viterbi trellis. Its easy to fall down the rabbit hole of trying hours of optimizations that fail to lead above an 85 or 90% on these assignments. Std 17.314 1.886 5.573 First two projects are generally considered difficult and if one has less background with Python/Numpy/Algorithms/etc. I spent about 10 hours on it and was well above the median. Its a trade off but I felt like they were just extra hard assignments since you have no auto grader to even check how your doing. Full Document, International Project Management Association. For your own consideration a note note about me: I have writing code at my day job for over 8 years and still find coding for assignment time consuming. Very well run class, TA and professor and course content, all three outstanding!. The lectures are disorganized and are a mashup of videos from a handful of lecturers, making it confusing to follow. For this course, we use Bayes Theorem for inference on Bayes Nets and distribution sampling. Consider the videos to be a gentle introduction, and look to the textbook and papers for the bulk of your understanding. #4 and #5 were pretty straightforward once you fully understand the ideas and terminology. With the unittest provided on local as well as gradescope i was able to keep working on these not only to get full credit but also to not stop till i really got the concept. No room to relax. There is some probability, but that was about the extent of it. Sometimes, the problem simply needs more explanation. I feel that the final did not do a good job of assessing my understanding of the material; rather, it tested how many times I double checked my calculations. I took PTO for the exams here - I did spend about 20 hours on the MT and final. don't have to use gaussian_prob this time, but the return format should be identical to Part 1b. There were no class-wide cheating scandals in this course, at least any that were reported. I took an unusual approach to the class which is probably opposite that taken by most students in the program, but which worked very well for me because of my background (I was well-prepared for the class had taken ML, RL, DL, RAIT, and have a strong background in probability theory). Despite this, I was able to get a solid A in both classes this semester while also studying for and earning an Oracle OCP Java certification, picking up a ton of extra PT job shifts, and spending an entire weekend cleaning out my garage. shortest path, A* search, decision trees/random forests, unsupervised learning (clustering), hidden markov model, etc. Very comprehensive coverage of traditional AI techniques, so it sort of lacks a coherent thread through the course (just a lot of material to cover). I felt this exam was more challenging (on average) than the Midterm mostly due to challenging Bayes Net & Logic questions. It didnt help that they scheduled 208 pages of reading from the textbook in the last 2 weeks. They varied from decent to incredibly confusing depending on which professor was teaching. This is all in addition to them being incredibly helpful on piazza as well. If you are strong in Python and Calculus and can put in time for the assignments and exams, I would definitely recommend this course. I think the exam should normally take around 15-20 hours if done very carefully, but because of the constant checking of assumptions and poorly worded questions, the time to complete doubled. The textbook is fantastic and offers a wealth of deep content that will help you understand the material more thoroughly. Youll be learning for the first time about search algorithms then next thing you know you have to do multi-directional searches with landmarks. It doesnt go very deep in any particular topic, but gives you an excellent survey of different techniques. This is really great class but slightly heavy on workload; I have spent over 30-35 hours every week but in the end it was worth it. They were long (each took me 15-20 hours and I could have spent longer) and each one had some topics that were literally just a mention in the lectures. Unlike many classes where the starter code provides a lot of the minutia functionality, this class you pretty much build from the ground up. hmm_submission_tests.py: Local test file. I was curious about how artificial intelligence would be defined in a formal education syllabus. Its as close to a self-study course as youll find in this program (and thats saying a lot). The second assignment was especially challenging. In the second half of the course, to be honest, I stopped reading the book. Also, having previously taken ML4T and AI4R made the hard projects in this class pretty easy. This means so long as you got a full mark (not quite hard to achieve) in every assignment (accounts for 60% in Summer 2017) and dont bomb the midterm or final (accounts for 20% each), youre good. The class is an overview of several major components of AI: Game Playing (Minimax/Alphabeta), Search (A*), Bayes Nets/Probability, Decision Trees/Random Forests, and Hidden Markov Models, with some machine learning and other topics sprinkled in. I would call it more of a project honestly. One can go shallow or deep with the material and extra study/assignments - impacting learning outcome, but not necessarily the grade. Same videos are just added as part of this course. However, Bonnie wouldnt like it and you would be stuck with WHAT THE HELL IS WRONG HERE? The videos are pretty good, but they do seem patched together, with several different lectures and styles. Just for those who dont know the difference between AI and Machine Learning (that was me! I actually enjoyed A1 but A2 was a nightmare. The projects were interesting and helped me understand a certain topic very well. Hopefully on future iterations the TAs/Staff will figure out how to lock it down so that they dont have to worry about future students finding the previous class forums. Peoples time and money are worth more than the bs sense of having put positive vibe into the universe by writing a bs review like this. They are all concerned about implementing the learned algorithms / tecniques. This course is heavily focused on the projects and exams. The exam length was twice as long as the midterm, but the time to take the test was the same. Well I was wrong. It can be said that the extra credits saved my ass. Notice however I always started working around 5 days after each assignment was posted. You are asked to use the provided function gaussian_prob to compute emission probabilities. The bad: These are actually uniquely interesting (and long! These involved implementing some popular and fundamental AI algorithms from scratch, including: These assignments also included opportunities for extra credit. You have just completed your final assignment for CS6601 Artificial Intelligence. First: a huge chunk of the material on the exams were never taught through the lectures or the textbook. They removed 5th assignment extra credit and compensated with 6B + extra grade. Big high fives to the TAs for getting grading done quickly. . I guess the takeaway from my word vomit is that this class has a lot of inconsistencies. The assigments were all challenging covering various areas of AI. The first two assignments are very time consuming and difficult to get 100%, and after those two, it is quite straightforward and fairly doable to achieve 100% and even extra credit. As others have mentioned, the first two homeworks are the most time-consuming in that there are several nuances to the projects that need to be tackled in order to achieve full credit. The feeling of getting a 100 on GradeScope after grinding it out for hours and hours over the course of a week and a half is fantastic. Not hard, but can be tedious. Knowing numpy in advance for will help for P5 and P6 but its doable to pick it up on the fly. I suspect that many in the class are just that smart - bordering genius. books was good (as much as i could keep up with reading it) but also there were a lot of resources online to help, TAs were great help during office hours and on piazza, love coding in python and this was all in python. Dr. Ploetz is an interesting guy, obviously brilliant and helpful, and very approachable. Both the midterm & the finals are open book format & we had a full week to submit. I spent ~12 hours on the final and probably needed another 20 hours to get my desired score for an A but alas, here I am. It is hectic if you take the course in summer . The highlight of this course is the 2 exams which are open book for a week. My big suggestion here is to take a stab early, then the weekend before the exam is due redo the exam with the clarifications, and figure out why any answers are different. There is a good class hiding somewhere in the course materials, but it wasnt on display in Fall 16. Now I was trying the minimax assignment a bit but again the documentation in their code is unclear. Heres how you succeed - the lazy way. 9 On project 3 (Bayes Networks), I only got to 85 after 37 hours and 20 submissions. On assignments, there were six assignments that were each two - three weeks long. Aim for >80% on at least one of A1 & A2, then aim for 100% on the remaining assignments. but you can easily spend 40 hours on them and have to take PTO from work. Highly recommend. Georgia Institute of TechnologyNorth Avenue, Atlanta, GA 30332Phone: 404-894-2000, Application Deadlines, Process and Requirements. To the reviewer below mine again. Start early but not too early. 1/23/2018 omscs6601/assignment_1: Assignment 1 for Artificial Intelligence 2/6The Game The rules of 2 Queen's Isolation are simple. Have just completed the exceptionally difficult and rewarding course on artificial intelligence, just as my new role involved putting a healthcare data product into production (press release here). There was discussion of this being due to Piazza. I am a programmer, but have no statistics nor linear algebra experience. The take-home midterm and final exams basically serve to fill in the gaps by having you understand certain lecture topics more deeply some involving concepts related to the homeworks directly, tangentially, or not at all as different topics. I recommend taking it by itself if you suck at following directions and being a good student, or if you have minimal math and CS backgrounds, or if you just want to have time to deep dive into the topics while the class is still in session. Like previous semesters, the lowest assignment grade (out of 6) is dropped. I would not pair this class with another unless you have plenty of time to spare. My bed time shifted to between 2 and 3 am nightly because of the projects. That doesnt really do a good job of assessing your knowledge. I signed up for this course literally in the last minute on free-for-all day, not because it was hard to get into, but because I couldnt get into my top choice for the 3rd course. There are a TON of TAs, there are office hours every day (Dont expect quick answers on piazza, the threads run into thousands of posts), they seem to actually care to answer your questions (as opposed to the usual - implement the algorithm answers), the lecture videos are nice (pretty girls help), you learn about shark bites - all in all a good time. While we prefer you use 4th, youd have to translate the chapters on the syllabus to the 4th edition, so you can use either as long as you cover the material.. This course is run in a way that it pigeon-holes you into only focussing on this old age assignments they assign you. Its odd and disappointing for a class of at least 600 online + 200 on-campus students (minus the droppers) to see this level of activity. If you dont start assignments early, you will drop this class or ruin your GPA or wont graduate (if youre in the Interactive Intelligence track). For a class this large, you will mostly interact with the TAs for the day-to-day, but he is around and active if you need him. I was a little disappointed in the presentation and instruction in this course. For many questions, if you make mistake in the first 1 or 2 steps, all subsequent steps will be wrong and you will loose all marks. Just save yourself the money. Gives a good perspective on non-ML approaches to AI, which basically means search algorithms. The length is not a huge issue given that it is twice the material, but there were no real deliverables in the prior week. The format this semester is exactly the same as previous years. There are many online links to get more detailed information for completing assignments. This course is not for the faint of heart. If youre looking to take two classes and have taken ML4T and AI4R already, it is 100% doable as long as you find a way to manage your time on exam weeks. There were also other interesting topics covered such as constraint satisfaction, logic and planning, and optimisation which I found interesting and useful in expanding my knowledge in algorithms. All assignments were submitted to Gradescope and most you could keep submitting to test you could, though two of them you had limited submissions but better local test cases. View At minimum, I would recommend taking ML4T before this. What you get on Gradescope is your score which makes life a little less stressful. The only reason I might advise taking this course later is some students came in with machine learning class experience and that gave them a small advantage (e.g. hmm_submission_test.py Labs that took me 40+ hours took them maybe 10 hours. Its just not the type of problem you can solve quickly on StackOverflow. I really like the way instructor setup the exams. This is another reason you had to use a calculator alot to do things like log10(x**(y/t)). The book is great for the first half of the semester, and ok for the second half. The assignments are long and I spent probably 24 + hours on some, but you get two weeks to do them. It can often be used to replace the lectures in this course. Come on guy. However, I found the Search topic in general very unique to this class and am very glad to learn it. The first 80% of the project is relatively straight forward but the final 20%-ish is much more focused on researching and experimenting with actively researched problems. Several OMSCS classes . The TA and way class was run was fantastic. All of the assignments required a decent chunk of time (10+ hours over 2 weeks), and all of them were doable. Your classmates are insanely smart and/or hardworking. In terms of grading, you should be able to get 90-100 on assignment 1, 100s on assignments 3-6, and Id highly recommend skipping assignment 2 after learning just enough about BFS, DFS, and A* search. The notion that was stated previously that they dont care is completely false and unfair to them and the effort they put in to our learning experience. This is very good class to overview all AI concepts and some of ML with deep enough mathematical details. Seriously. I have no idea why - probably he was on summer vacation and handed everything to TAs. That is if the overriding goal of OMSCS is to spread learning and knowledge about all these new topics. I found the assignments on decision trees and expectation maximisation to be somewhat easier, though HMMs were unfamiliar and required more time. Just average in every way, thats me -right in the fleshy part of the gaussian. I attempted this course in Summer 2020 and had no idea what I was getting myself into and subsequently dropped before the midterm. The assignment and feedback are big part of the learning for me. But, I did not like this course. It is definitely in the top 3 courses in the program for me, and arguably #1, but in a way that makes it unique. Its a better class if you prefer independent learning and showing off your superior knowledge and skills to the professor. HOUSE 15 Pretty low stress overall and satisfying (never thought I would call an exam satisfying). We are almost at the end of the course. Since there is so much material and only 16 weeks to learn them, the course does not go into depth on any of the topics. I learned a lot about the methods used in AI from the assignments and even the final exam. The class covered a ton of material in a very short amount of time. All in all, highly recommended if you have any interest in AI. They are take-home exams, you have a week, and you can use materials from the class. I tracked my time spent in the class using a focus timer app and averaged 15 hours/week with a few heavy weeks of 20-30 hours for the search assignment (1st), gaussian mixture models assignment (5th), and final exam. What is the probability that the squad will have, A text file words.txt is given, which contains several words, one per each line. One of the 6 assignment grades was dropped, as per the syllabus. Assignments take between 10-20 hours (assignments 1,2,5 were around 20 and assignments 3,4,6 were around 10 to 15) and you get about two weeks for each. Highly recommended, much better than KBAI, just be prepared to work. This class definitely has its challenges. I recommend taking it regardless, but if it is one of your core classes, maybe get a couple of other difficult classes under your belt first (like CV or CP) before taking this one. Are you familiar with the basic concepts of linear algebra, probability, and single/multi-variable calculus. I never went to office hours, but they were hosted regularly as well. The weeklong open book/open notes nature of the exams means that they really make you dig deep and earn every point. It makes materials which supposed to be fun and interesting extremely boring and dry, and makes me fall asleep as soon as I hear the lecturers talking. Due to my heavy workload (in my startup), I could only start on assignments / exams about three - five days after they were released. This class is worth it if you have the time. One thing that impressed me the most was Thad spending 2. For summer session we only had one final, and while it was challenging I do think it was fair. 1) Take Berkeleys CS188 MOOC (or equivalent, which covers same material (https://courses.edx.org/courses/BerkeleyX/CS188.1x-4/1T2015/course/), 2) Start A1 and A2 before class starts (they are the longest; all assignments are posted in Github at the start and dont change much year to year, so you can either start the class and drop or find the current repo), 3) Finally get comfortable with Pycharm, unit testing and TDD debugging in this class is super hard and kills your time the more modular you make your programs and the more you use industrial strength IDE like Pycharm, the fast youll be, 4) Plan to take week off for each exam (they are week-long I took a week off for A2 and for the Midterm which earned me enough points to need only 40% on final), 5) Form a Slack study group. Mean 56.300 37.110 50.000 Each topic comes with homework. Machine Learning for Trading involves learning about machine learning on sequential data, with lot of Numpy vectorization goodness. Unless you have a lot of time on your hands I would not recommend taking this course as an elective. It covers a lots of AI materials that can be used in other computing disciplines as well. However, since the teaching staff modifies the problem slightly each semester to mitigate plagiarism, the tests used to evaluate the implementations become broken. Some of the final exam questions, I simply had no idea what they were asking. After assignment 1, unfortunately, everything went downhill. A good understanding of probability will also help and will make Bayes Nets much easier. An interesting application, for which we had to solve a mini-version of, is multiprocessor scheduling.
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