verb phrase such as "BUY CAR" and "BUY HOUSE". My local tests were failing but somehow got 100 in GS. So, prepare before the semester begins; you will see the course lecture when the semester begins but for early preparation go through: Now when you see the course material, it wont be first time. This meant the questions were pretty open ended & challenging. other fields. Will you learn something? I managed to get a 96% before the curve. You have to implement some popular AI algorithms from scratch. On-campus section received additional lecture content. They provide some of the answers inside the test so that you can confirm if your method is correct. If one has less programming background, consider preparing by learning Python/Numpy, a bit of search algorithms and probability before starting the course. This is very good class to overview all AI concepts and some of ML with deep enough mathematical details. The two exams are take home packets with word problems you can solve by hand or by code. Can you use some addition videos to explain the classic algorithm in demo and problems? The class takes plagiarism very seriously and you are not allowed to look at pseudo code from anywhere other than class resources. Additionally, they will even hold code reviews for you and actually look at your code and give you pointers or ideas. The best five contributed a total of 60% to the total grade. It was not beneficial to start the project early (because of the errors), but it also didnt always pay to start them too late because they would often make changes to the assignments after they were officially released. But if you are weak in those areas, then I recommend taking another course before this one or a refresher before the class starts. Overall a great class. Strongly consider not taking a second class, despite that pushing off my graduation plans. The final had 60+ pages for 10 problems. I agree with the previous post(s) that final exam is difficult and seems to be largely unrelated to projects, but I take it as a necessary step to cover the other chapters in the AIMA book. {7} After the course you will gain an opportunity to do research in 8903 Special Problem. This is my 5th class in OMSCS. A lot is covered which will expand your problem-solving skills. Trust it. Even though it covers such a wide breadth of topics, the course does fair amount of justice to them. Heavy workload, but no pain no gain. The curve was a 90+ so they made it a 90, no rounding! Overall, the class is really excellent, and really one of the best in OMSCS. That may have made some of these assignments easier than in the past. Thads level of interaction with the class on piazza and in the office hours really made you feel like he cared about your understanding. I do realize that the previous terms seemingly had a longer exams format. The next training sample has the following observed sequence: An A is above median, B at or below. However, with enough effort, it is more straightforward to achieve full marks with these (but dont start too late!). The other weeks I definitely slacked and put in <10 just watching lectures. Ill proceed by briefly listing pros and cons. 4) The biweekly assignments can be interesting. The course provided a good overview of the fundamental AI techniques such as search, Bayes networks, HMMs, etc. I thought the book was very good, but we only really dived deep in a few areas. When they give you a week, you will need a week. You can view the lecture videos for this coursehere. One of the best policy of this class is one of the assignment with lowest grade will be dropped. This course is one of the monsters in OMSCS, but its doable. The final 3 assignments had very little to do with the final exam which was surprising to me. Some of them are pretty complex. This will definitely help you to succeed in both the assignments and the exam. Never seen that rule before. I found in the weeks that I wasnt actively working on an assignment, the classwork was pretty light. The TLDR is that it is not an easy course, but not that hard if you have experience programming and are willing to put time in. My lessons learned and advice is to complete the lectures right away to let the concepts sink in and start the assignments early. There were some questions posted, but answers may not come for a couple days. The material is really awesome stuff. The lack of communication was a recurring theme, culminating in the frustration over the final exam. I had to scour the internet looking for more palatable youtube videos and articles to understand the concepts. I thought I prepared throughly for each exam, but there were always a few questions out of left field that required me to pore over an obscure section of the textbook and learn on the fly. This was a great course and one of my favorites in the program. He rarely posted on Piazza. This article provides a cool visualization on the backtracking search tree for sudoku. A few of the sections use videos from the older intro to AI course but I think those will be updated. We are given training data, which consists of the original word and its sequence. Added notebook and changed tests *Note I spent at least 20 hours on each one. First two projects are generally considered difficult and if one has less background with Python/Numpy/Algorithms/etc. Such design of the examination is very thoughtful. Exams are hard! I really did not like that some of the assignments starter code was difficult to understand and sometimes TAs would have to go back and fix bugs after the assignment was released. Start early if you can and dont hesitate to message the TAs. The TAs were helpful in sorting out issues like this but it seemed like this problem should have just been avoided to begin with. One assignment is due every other week. Video lectures are a joke, reading material is difficult to digest, assignments are out of context and unnecessarily complex, and exams are largely based on topics that are covered with 1-2 minutes video lectures at best or are out of context just like the assignments. Recommended. Ive never considered myself strong at math in general and after looking at the syllabus, I knew I was going to have to work more than others in order to complete assignments. Its so much information its hard to absorb in one round. The textbook is a useful resource also. This was probably the hardest course Ive taken in the program. You are only allowed to refer to the book and lectures. The midterm felt like an extension of the projects with a couple sections with non-project related content that held your hand pretty well. 6: My assignments average was already high enough and since I was behind in the lectures I used the time for studying for the final. Then when we got the answers there were more mistakes in them and the exam was re-graded for everyone to account for that. You are only given a week to complete and they are given concurrently with assignments. Some of the questions are fun and they feel like teaching more than testing. Staff: In my opinion, the TAs did great. Lots of theoretical contents, insufficient local tests and just 5 Gradescope submissions. There was a lot of self-learning, and learning from peers and TAs on both the Slack channel and Piazza. Challenging, frustrating, take a lot of time and the finish is very rewarding if you move your figures on the keyboard just right (if you get your code to work, what were you thinking?). Good at recursive algorithms? But then Dr. Starner was on sabbatical at Google all summer (working on Google Glass). TL;DR I found reading the book, then following it up with the lectures was helpful to confirm my understanding of the material as the lectures are much simpler. You will need solid stats and linear algebra, and then you may have an easier time in the latter half of the course. I skipped Fall - AI to get to Thads class but was extremely disappointed on how the class was run. It is a great class, you will learn and grow a lot, but it is a ton of work and a lot of stress. he led the data science teams at Lazada (acquired by Alibaba) and uCare.ai. I work fulltime and had time to go through the exam three times to check my work and ultimately get ~94/100. They dont do a good job explaining subsequent assignments, and much of my time was wasted trying to figure out the assignment instead of understanding the lectures and reading the book. {6} Course is trying to be wide and not deep. This was my third class in the OMSCS program, my first summer course, and I took it alone while working full time. {3} Videos are high level and give only basic understanding of topics, a lot of readings are required to clarify finer details. Good survey of several types of algorithms and concepts used in AI such as game playing, graph search, Bayesian networks, Markov models, and some machine learning. These are seldom covered in other online courses available which tend to mostly focus on machine learning. Im quite terrible with probability (specifically Bayes) so I bombed that part of the midterm, and since the final will be comprehensive I fully expect that be brought back to embarrass me again. I learned lots, the lectures are fun and the assignments are interesting. The midterm and final are a bit lengthy, but not too difficult, basically felt like the workload of another assignment, but with only 1 week instead of 2. Learn Git and GitHub without any. Std 17.314 1.886 5.573 Definitely is a differently structured course than those mentioned before, but overall I think it was a good class. Its a lot to keep up with. I had no intentions of taking this course but since I signed up for it I treated it like any other course that I take. In terms of difficulty, I found the course to be fairly difficult. Oh and the exams (mid-term and final) were take home. If you were like me, a professional developer in the industry for years, you will find its easy to pick up python/numpy as part of your 1st assignment. First off its take home, open book, open lectures. The assignments are time-consuming but fun and enjoyable overall. On the other hand, they are 30 hour long tests. Instead of acknowledging the mistakes and thanking students for pointing them out, they would get defensive and write things like that will also be accepted because we didnt specify how to do X. I had read many reviews that scared me and almost made me withdraw from the class before even starting. Total 6 assignments. It can be said that the extra credits saved my ass. He hosted office hours before each exam and that was it. Peter Norvigs videos were ok - made with home video but with good explanations. The worst part here was not the amount of errors, which was not insignificant, but the attitude of some (not all) TAs. Recommend not to get too hung up by it. Gives a good perspective on non-ML approaches to AI, which basically means search algorithms. Again, I came in completely new to everything probabilities related and was able to complete the assignment with a 100% only using 1 week of the given 2 weeks. Book: During Final week you HAVE to be watching Piazza regularly for updates. Getting to an A will need to be a little more work on the tests. I found the readings easy to understand due to how the amazing job the writers did. Projects are frontloaded, with Adverserial Search (Minimax/AB Pruning/Iterative Deepening) and Search (A*, UCS, bi/tridirectional) being the hardest and most time consuming. Like I said in my advice, if youre interested in the topic and you feel you have the prerequisites covered, absolutely go for it! The test suite the staff provides can either be sparse or very comprehensive. Really hard class, first 2-3 projects are tedious and parameter-tuning dependent but afterwards it gets better. Here is my advice: Prepare for heavy self-learning. Overall, this is again great class to take. Piazza is just a circle jerk of who finished faster. The first 2 assignments are extremely time consuming, and the midterm and final exams are beasts. Exams were heavy on calculations. Best of luck in the class and hope you stick through the pain and relish in the accomplishment when you are done. There are 6 assignments, one of which is droppable. Assignments: These were the best part of the class. it is good learning exercise though not easy to get the bonus point. There was so much work to be done, and with a full-time job, it was pretty crazy getting the time to do everything. The lectures are easy to get through and honestly fun to watch. The midterm and finals were 34 and 47 pages respectively, with seven days to complete them. The clarifications thread was longer that Rapunzels hair. If you decide to take the course, just go in expecting things will be challenging and try not to give up at the first barrier. (One assignment involving MCMC is covered in the lecture for only 90 seconds). The unofficial slack was the saving grace for this course. But went on forever. Cons: TAs really slow and/or unhelpful. But if you do well in the projects like I did, you dont have to do that well in the exams to get an A. this is the most ridiculous course I have ever took, it is simply not designed to help you learn but make you miserable. Overall, excellent class and really a must-take. You can game grading this way. The midterm was ~28 pages (much of that is explanation or diagrams) and was a week take-home. However, provided you put in the time in the readings and research, its almost impossible to fail in this course. You have to get perfect score on almost everything and hope that some others do not, in order to get an A in this class. The projects are pretty good (as in, they cover helpful topics in an applicable way that is interesting), but the experience you have with them will most likely be a little rough at the beginning (mostly the first project) of the semester and get better over time, as the first few projects (at least, in their current order at the time of writing) are re-written each semester. The midterm and final are take home, and you are given a week to do them. The 4th is definitely a more relevant edition. Some of them even required a Wikipedia link in order to solve since it wasnt in the lectures or textbook. Just another 168 hours of trying things, reading things, so much scratch paper, so much googling. Assignments were relevant to the course material and did a good job pushing you to learn the material thoroughly. If you get lost or stuck go watch some statquest videos, they will help. The class has 6 assignments and you get to drop your lowest score. The worst part of exam 1 were the endless revisions and clarifications. I dont do well with the cram everything in your brain for a test approach. Some are great and others not so much. It is useful throughout the whole course. it was announced at the beginning that grading would be based on class median anyone above the median would get an A. 7 days to complete each exam is difficult. I dropped right after the assignment 1 (search) deadline because I felt as though there is not enough teaching happening unless you ask questions on Piazza. I took it with CS 6340 (SAT, another great class, and is not about unit testing). Eugene Yan designs, builds, and operates machine learning systems that serve customers at scale. if you are just thinking of not wasting the tuition fee therefore push yourself on catching this course. How in the world do people care about extra credit once final exams are over is beyond me. the reason I said so is that since spring 2019 it got changed to using the auto-grade system which is a complete freaking black box, all you got as error messages are time out, you lost etc. Unformatted text preview: 4/1/2020 omscs6601/assignment_6: Assignment 6 for CS 6601. I read everything but receive too much to respond to all of it. Some extra credit is limited to top N scores on the assignment, so not everyone can get it. Project 1: Project one was absolutely NOT straight forward. Thats this course. There is huge gap between the easy-to-follow videos and the difficulty of some of the handouts. There were 6 assignments in the Fall 2017 class; you get to drop the lowest grade. I had to take a days vacation for the mid-term and finals each. The material is challenging but fair and often fun. Each exam is a take home, week long assignments. I spent a lot of time in Search and the last one HMM (use up the full 2 weeks, 40 hours+). The difficulty and workload reviews I see on this site were way above what I experienced. The TAs and professors were very engaged, responsive, and helpful. Most projects just have a smattering of academic research PDFs that are given as the basis of where to start learning on your own. I went in with poor probability skills and no AI undergrad class, so Ive been spending a lot of time on each project. The breadth of topics that you are supposed to learn in the class is honestly probably too much to realistically retain for 1 semester, but the topics that you do drill into should stick. in 6601, I felt I was completely screwed by the instructors simply because they want to make their lives easier. If you keep re-reading the articles and looking at formulas with strange symbols, they eventually start to make sense. Use the training samples from the table below. I learned a quite a bit by just doing exams. Conceptually easier than A1 but however tests are more strict and is more difficult to get a full score. Piazza is great but just a BIT too slow and indirect when you have scarce time so find a group in the intros page of people that seem to care, and ask them to join a slack group, 6) Know Python and some linear algebra in numpy honestly, I cant imagine taking this class while having to learn Python or numpy or linear algebra just REFRESHING myself on some of those was hard enough. The assignments are fun but dont relate to each other so you will learn a lot about the subject then completely erase your memory for the next subject. My background is a CS major working as a software engineer at a FAANG. I consider myself an average programmer with below average math skills. The week of the final exam was probably the toughest week I had. 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. I actually decided to take 2 days off from work (Thurs and Fri) the week of the final just to make sure I had enough time to work on it, and Im really glad I did. B/C cutoff is placed at median minus 1 standard deviation or 80%, whichever is lower. One assignment I particularly enjoyed was decision trees. CS-6601 is a great introduction class to AI. For those of you who are limited on time to devote to the class as I was, my recommendation is to watch all of the lecture videos prior to the start of the semester, and then read and appropriately skim the textbook based on the provided list of topics for the midterm and final, which would provide a much more manageable workload. I think its worth mentioning the curve is actually not helpful to get an A. Decision trees (20 hours) - Relatively straightforward. That is not the case for this class. And yes, it really does take 40 hours. . 4) You get to drop one of the six assignments. 0.1234 rounds to 0.123 2 take home exams that are also very hard. This course may impose additional academic integrity stipulations; consult the official course documentation for more information. hmm_submission_test.py That probably means youre fundamentally misunderstanding something and should take a break. about data/ML systems and techniques, writing, and career growth. Youll have a much better time in this class if you just read/understand/follow the directions. The instructor and TAs are very good, suportive, responsive and active. Be comfortable reading math symbols/equations. Similar to what a number of other posts have covered. The assignments were good, but the last parts would get challenging. Especially the final. The exams were take-home and open-book, which allows you to use the exam as a way to learn additional material you would not have otherwise. I have completed 5 other courses in the program (KBAI, AI for Robotics, Intro to Health Informatics, Computer Networks and Educational Technology) and this one was by far the most time consuming. The lectures are a mixed bag. If you want to learn AI, search for CS 188 video lectures from UC Berkeley, and see the difference in lecture quality yourself. I really liked the format of riddle/thinking problems in this format. py files they provide werent perfect - lets put it this way. Here is one from Spring 2021. I actually enjoyed A1 but A2 was a nightmare. The projects do reflect the material and give a real world application. The lectures are a patchwork of old content from Thrun and Norvig and some new modules with Starner; production value is good, but the new content is often superficial, and it seems to be read from a script. I will highly recommend this course, since it really makes me learn a lot of things and realize OMSCS is an actual graduate program, dont expect anything easy. The TAs are hand-tied but did the best they could. Grab recent semester syllabus and go into course schedule. There is so much material to cover in this class, there should be no reason for adding material never taught. And the grading is based on how fast your solution works, not a clear right/wrong answer. Functions to complete: Your fellow classmates will reach a 100 in the assignments and drive the median up, so that means you have to as well. Like many others, I have mixed feelings about this class. Overall A is possible if you put in the effort and B is a no-brainer if you get 90+ in the first two assignments. Occasionally, you need to write your own unit test for the assignments. Seriously, why does everyone keep thinking a graduate-level CS program should have the same structure as a high school math class? One mis-calculation and its all over. Thad and the TAs were excellent in every way. Give yourself 5+ days for the take home final - you could do it in a day but the time to check work and not rush helps a lot, especially considering how important the final exam is to your grade. There is probably a higher number of topics in this single course than any other I've taken, though the depth within each varies. Project 2 - Graph Search, Djikstras, A* - good lab, and straight forward. 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 math wont work in your favor and more than a few students realized this too late. This class is very difficult and very time consuming. One of the things I liked the most is Bonnie (autograder) for projects which will make life easier. To me, this seems incredibly lazy and just pathetic. 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