Most of the TA office hours are used to explain assignments. Some projects took 10 hours while others took up to 50 hours due to my background. Students are disinterested and TAs also dont seem that interested or knowledgeable about the content. I went through the Docker-based setup and couldnt quickly figure out what the actual work was so I didnt bother. In particular, this project employs hidden Markov models (HMM's) to Towards the end of the class I started falling behind on the readings. So my advice is just not to worry so much about the score but rather, enjoy and focus on the knowledge you will gain from this great course. That is all fine, but a comprehensive course like AI can provide maturity to someone starting in their career. I absolutely LOVE the joint model and hope more classes are offered this way in the future. Fourth: The lecture videos by professor Starner are awful and uninformative. We apply minimax and alpha-beta pruning to the game, "Swap Isolation", to beat various players, of increasing difficulty. We are guaranteed to find the optimal path from start to goal. In the end, the grey, yellow, two shades of blue, and two shades of red are found to be the average colors with the least error across all pixels. The instructors are ignorant and super unhelpful. You would benefit significantly by taking an introduction to AI course from the stanford online courses. question on the exams. Even if theyre outdated, it feels so gratifying to solve these problems, do these projects, implement these algorithms, etc. However, for those that breezed through labs way too quickly, I wondered if maybe they had a network of friends that were sharing assignments from previous semesters and/or working together. 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. I like Bonnie because it gives students feedback on their progress, so when you send in your final submission you should know what score youre going to get. 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). Because of the ample resources, I won't touch much on decision trees and forests, as this assignment simply involved their implementation. If you are expecting AI opportunities or expecting something to happen, youll be disappointed. The lectures themselves vary from excellent to very poor. You will most likely drop Assignment 1, if any. In Part 1a, we use only right-hand Y-axis coordinates as our feature, and now we are going to use both hands. Subject itself is good. Thats all the info you need. As others have mentioned, there are 6 assignments (highest 5 count towards your final grade) and 2 take-home exams. The course took more time than necessary to learn content because it was very much self taught regarding details. While I loathed the exams, I learned a lot from them. Searching algorithms - BFS, UFS, A*, etc. Each homework assignment was a coding project, spanning two weeks. Before taking it, you need to have a good level at statistics (I would take a look at the Khan Academy, or the MITx course), and having a good/deep understanding of Python would also help. . some people beat it with vanilla alpha-beta pruning while others couldnt with iterative deepening. The problems on the final were extremely tedious, because if you made one small mistake (miscalculated something small), this could snowball and make you lose points on the entire problem. Conversely, it would probably be impossible to convince me that any human being has ever learned anything from Thruns lectures in this course. The bad: The final was brutal. Overall exams were alright but stressful to keep up with the changes. If you have the time and interest, I would recommend giving it a try! Despite a dedicated clarifications thread, it still contained questions which could be interpreted differently. The lectures are shallow though as they try to cover so much, youll need to do the recommended readings otherwise you will struggle with the assignments and the exams. They care. Lectures: The udacity lectures were not that great. Artificial Intelligence covers relevant and modern approaches to modelling, imaging, and optimization. My previous courses were CS 6250 CN and CS 6340 SAT. I realize that TAs have their own projects which take their time but when a student takes time to ask a well thought out question, replies from TAs like yes and no dont really cut it. I have a BS and MS, but both in engineering fields. Overall, this was a lot of great, foundational content packaged in one semester. We get to code a lot more on the algorithm and understand its complexity. TAs: This part is convincing me that this review must have been written by TAs. HUGE assignment, unforgiving autograder, very hard to avoid all edge-case bugs. So the large chunk of your time is spent on these. By far the most frustrating and unprofessional experience I have had in OMSCS. 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. I believe a big portion of students will get A given the grading criteria (>90 will get A). I did not score 100 but half of the problems are presented in a way where it is very difficult to screw up. 2 days ago 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. With a 95% average on the assignments and no extra credit (only the Decision Tree extra credit is easy to get, everything else is hard), I needed a 45% on the final to get 80% in the class and come out with a guaranteed B. As soon as you submit the previous assignment, the next one is released and back to work you go for hours upon hours. The lectures are a mixed bag. The best part for me though was being allowed to submit the assignment multiple times and have it graded each submission. 1. The other course would involve keeping up with the extensive learning material, such as lecture videos, textbook readings, and supplemental journal articles in order to prepare for a midterm and comprehensive final exam (taking me 17 and 24 hours to complete, respectively) that are structured like puzzles and brain teasers loosely based on concepts from the material. Also, the longer Im in this program the more Im realizing theres quite a bit of hoiti-toitiness, humble-bragging, begging for remarks, etc which I never saw during my undergrad CS. I could say the difficulty level of this class is hard for the first two assignments, but later on it is about medium or somewhere less than hard. I am sitting between a B and a C. If I blow the final, we will see how low the curve goes. Considering I took it in the summer, I highly recommend students save this course for the fall or spring, and take it as their only class. Definitely read the chapter 13 & 14, probability and bayes net (BN Representation) before semester begins. (I got >90 on both) But the exams are riddled with typos, grammar mistakes, ambiguous problem definition, etc. Additionally, they will even hold code reviews for you and actually look at your code and give you pointers or ideas. Piazza and slack were very helpful when it came to understanding how to do things. There was one where they just linked a YouTube video and told you to follow it. Have taken SA, HCI, and CN. This increased my excitement in learning about the fundamentals of using math and logic to solve difficult problems. I echo the reviews below mine about this being a survey class on AI. The projects could be a breeze if your mental model matched that of the TA that wrote the grader. verb phrase such as "BUY CAR" and "BUY HOUSE". The algorithm was neither clear nor straight forward, and there wasnt a lot of instruction or guidance to make it straight. The lectures are lackluster and the exam experience is horrendous. Ive gotten As in all previous courses with a healthy balance of proper planning, weekends, and evenings, without making a ton of personal sacrifices for school work. Most parts of the class were polished and in combination provided one of the best learning experiences Ive had in the program. The gif below shows the clusters from k = 2 6 over the original image, on the left. The instructor and TAs are very good, suportive, responsive and active. The first project nearly killed me, but it was worth it in the end. If you want to put in the extra effort for 100% - go ahead and burn yourself out. with no obvervation for State 1. It might seem a lot of time, but it is not, you might pretty easily run out of time, as in the next case Make sure to ask instructors any and all questions you might have. Assignments are good with well defined test scripts and Gradescope tests. Exams are take home, but are extremely hard and time consuming. Youll have a much better time in this class if you just read/understand/follow the directions. The lecture content is useful in some places, but you will need more. This was my third class in the OMSCS program, my first summer course, and I took it alone while working full time. are very foreign concept to you consider going through. 4) You get to drop one of the six assignments. I love Gradescope and it was well utilized in this course. The instructor and the TAs were all awesome and very helpful! It provided an overview of AI concepts and techniques that I found challenging and rewarding. It was tough and you got a week to do it. I honestly may have given this class a Liked before the final, but it really left a bad taste in my mouth. Exams are open book with about a week to work on them. 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. It was unnecessary to do this. I really like the way instructor setup the exams. The book in this course is a must, no matter which edition you get. 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. The no online resources allowed policy. Ran into a nasty bug that prevented assignment completion and dropping the course. BUT our exam was full of mistakes. I mostly used it as reference to do assignments. I did like Sebastian Thruns and Peter Norvigs videos. You will learn a lot from this class and I strongly recommend taking this class. It was easy, and I finished it surprisingly fast, but its a very uninspiring way to teach something as fun and useful as DT and RF is. About 63% of students who finished the class (didnt withdraw) received an A. Test became about doing complicated calculations rather than understanding concepts and I found to be the most useless part of this class. Sadly 30-50% of the time i learn concepts better from other resources about the topics. If you want a survey of classical AI, go for it, but do extra credit!!! But as review, I would also like to give some suggestions to the teachers and the students of this class. They cover all of the topics you read through, unfortunately you have to learn as you go. Basically begins with your classical AI problems of search and game playing then transitions to some ML topics such as GMM and random forests before getting into HMMs and some more theoretic parts of AI (planning, logic, etc.). I dont do well with the cram everything in your brain for a test approach. {6} Leverage your Assignments code to double check (or obtain) exams answers. I front-load most of the video lectures prior to the start of semester which helps me to save some time, There is not much discussion in Piazza. Unless youve got a 100 on five projects, dont think that you can skip one. Best class Ive taken so far (out of 4). So overall a course that covers a lot of interesting content but as others have said there was too many topics covered in the course and it could be split into smaller courses. If A and B were still independent after event C occurring, then this logic would hold! There is reason for this course being rated both difficult AND highly liked by reviewers. The exams were brutal. Take a few days off work for the midterm and final, Take your time deeply understanding the book and supplemental readings - all of them. Overall the course was great, and I highly recommend you take it, despite the shortcomings mentioned below. Cons: TAs really slow and/or unhelpful. Has lots of free time and likes focusing on school, Has prior exposure or knowledge of AI concepts and algorithms, Anyone working, with a family, or other external responsibilities, No prior exposure to AI concepts and algorithms, Cannot self-teach from academic papers or math proofs, Start a new module: Lets introduce this topic with a quiz How is a learner supposed to approach a problem they have no exposure to yet? This really expanded my skills and learnt a lot! There were literally dozens of clarifications made on Piazza (under a clarifications megathread) for both the midterm and the final. This course should be just named as Python Programming. Overall, I am glad I took this course early in the program. Especially the final. C is a building being crashed. from our dataset: JOHN CAN BUY HOUSE 3/8 4/1/2020 omscs6601/assignment_6: Assignment 6 for CS 6601. I liked some a lot more than others, but all were great learning experiences. The exams are take home but that doesnt mean they are easy. Logic & inference is a confusing topic that was poorly covered by most of our resources, including the lectures & book (the problem was a variation of this Stanford problem). But more than too often, everyone was afraid of sharing too much detail to be tagged as plagiarism. In a fall/spring semester, there are 6 assignments and your final grade will account for 5 best ones. Recommended. Please review the following questions, if you answer no to any of them you may want to refresh your knowledge or practice the required skills prior to taking the class: Your system must be able to install the latest release of Python 3.7. Every assignment was the right amount of challenging and it always felt great to figure out that last piece to get you full points on GS. So if you are gifted or a genius, this review is not for you. HOUSE 15 It took 30+ hours of work. The first half was great at going into depth at each subject, but the second half was definitely rushed, as if we were running out of time faster than the instructors had planned. Most have been pretty poor, but I would not recommend this course to anyone under typical circumstances. I feel thats the a better way to run an exam because if there is a topic I am weak at, I can just read up and attempt to solve it. I spent the entire week on each of them, carefully going over them 3 times each and did fairly well. Please also submit your submission.py to Canvas as backup. People criticize the lectures in general, but I dont think thats fair. I really like this class but it was super difficult and I dont think its suited well for a summer semester. the projects come out as a work in progress/beta if you start early, prepare to be frustrated with moving targets, bugs, and changes along the way. Here is my advice: Prepare for heavy self-learning. I have nothing to add about the projects that are not in the other posts so I am going to skip right into the final. the projects are very interesting, but, unless you have experience in some related field, they will take a lot of your time. In the autograder, we will also test your code against other evidence_vectors . Easy to get A, since everyone with total score above median (computed before adding extra credit) or above 90% will get A, not mentioning 6 extra credits (which is effectively 30 points in a 100-point final exam) can be earned without overwhelming effort. Well I was wrong. As somebody who is by no means a math-wiz, but does have the foundational knowledge they suggest, anybody that is comfortable with the prerequisites should be fine. I actually took an AI undergrad course in uni, and it covered many of the same topics, albeit more superficially and this was a deeper dive than I had studied AI in the past. Each assignment takes more than 40 hours. And, dare I say they were sort of fun? Assignments are interesting and exams are ideal. I was kind of confused by people who started the final as soon as it was released and then complained about clarifications. Youll trick yourself into thinking you can do it. The questions did not really cover knowledge, but rather just ability to do complex math and run algorithms by hand. The TAs: Overall pretty helpful but many times refused to help on certain things I believe as ordered by the Prof. Also, no help during test and had a now work on weekend policy which was super annoying since everything was due on Sunday. First: a huge chunk of the material on the exams were never taught through the lectures or the textbook. The lecture videos are made to be interactive but is not enough to gain enough knowledge about the topics - I had to read Rusell-Norvig in order to gain information. Start reading the textbook earlier. There were questions where you spent three-four hours working on a two point sub-question (out of 100), while an entire question (10-12 points) would take half a day. If not, are you comfortable in learning a language within the first week of class? I quite liked the course as well. These are not one on one session; its more like a mob of people breathing into the mike, rushing you rudely to finish and hurry up while the TA reviews your code, says were out of time and that he will look at it off hours, and then two weeks after the assignment is done, he messages you back and asks if you got the help you needed, and then doesnt even wait for your response and marks the post as resolved. Pretty cool! {7} NN or RL knowledge will help with last Assignment and the Final. If it were only once or twice, no big deal. Exams are disasters. This is a very good introduction to every AI/ML concepts, particularly when you want to deeply investigate one of the AI/ML topics. Additionally, there was a long clarifications thread on Piazza that corrected many mistakes on the final. then, it is the other player's turn, so we assume they try to minimize our value. Tips: Start early on assignments and even earlier on exams. They are so much calculation and so easy to make a simple calculation error. Im fairly certain youll survive KBAI w/o taking CS6601. The lectures will get you intrigued about major AI topics, but certainly wont make you some AI juggernaut. You will get a chance to learn that material mid exam, which last for a week. Less good is that the median for the homework is often a 100 or close to it. There are lots of calculations and ramp-up problems where future answers depend on previous answers. Those two will probably run you 15-20 hours at least.
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