In total, I like this course, because of interesting assignments and enthusiastic instructor. The lectures help you read the book, so watch the lectures and then reading will give you a better intuition to get through some of the more mathy parts. 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 spent about 40 hours working on it and could not get it to pass Gradescope, even though local tests were passing. Prereqs: Make sure you understand bayesian probability well. My original plan was to take ML4T to learn python and probabilities and then take AI but, I was unable to get into ML4T so I decided to proceed with taking AI despite not having the needed math background and not knowing python. P4 and P5 both took me under 2 days. The last 4 were relatively straightforward and didnt take too much time. Highly recommended, much better than KBAI, just be prepared to work. You are only allowed the class resources and book for these exams. My 20 hrs/week is an average. Im not sure this was a great first course, but it did require me to get right back into school mode. You will learn a lot from this class and I strongly recommend taking this class. The XYZ_test. The type of student I think would succeed: The type of student I think would struggle: My general recommendation to anyone considering this course would be to disregard the modules, Piazza, and book entirely. However, if you are like me and feel uncomfortable not achieving 100/100 then prepare to spend dozens of hours in this assignment. There are also research opportunities you can apply to at the end of the semester. I dont do well with the cram everything in your brain for a test approach. 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. The largest problem I had (and in fairness, it seems many others did not) on the exam were the clarifications. I settled for a B because I didnt think I could get a 69 on the final. The assignments are very hard and take lots of time, and require very good knowledge of Python. I found the book pretty helpful in most cases and if I didnt feel obligated to watch the lectures, I would have just rather read the book. The assignments are long and I spent probably 24 + hours on some, but you get two weeks to do them. Overall, I really enjoyed this course. 1/23/2018 omscs6601/assignment_1: Assignment 1 for Artificial Intelligence 2/6The Game The rules of 2 Queen's Isolation are simple. Looking to learn from instructors or TAs on Piazza? Some people will say they took off of work for it. Also, jump on the CS6601 Slack channel. It provides an overview of the vast field of artificial intelligence and teaches some popular algorithms from different areas of AI. The material can be math heavy. Some of them (the first two) require more time than others, but they are all doable within a reasonable time. The midterm was ~28 pages (much of that is explanation or diagrams) and was a week take-home. Dont bypass extra credit questions if you have the time to complete them (some are very time consuming and others arent). init So I suggest you brush up on your python! I didnt get to do all of them due to life stuff, but I had a lot of fun with the couple I did work through. These are actually uniquely interesting (and long! Anyway, with that said, I didnt have to use the book a whole lot, but it definitely helped when I needed it and Im glad I bought it. Initial Sometimes, the problem simply needs more explanation. Take your time to do midterm and final, review once again to avoid silly mistakes. It is definitely in the top 3 courses in the program for me, and arguably #1, but in a way that makes it unique. Id imagine being able to concentrate on learning the concepts rather than learning Python too would be nice. This course was the first course I actually had to take time off work and, on multiple occasions, would spend entire weekends working on the projects and still only scraped by. Only need to complete 4/5 assignments (Summer), or 5/6 (Fall / Spring). Then, since everyone presumably got full credit for the homework, it means the grade is based on the two exams, which are RIDICULOUS. 3) ahead of course. There was a bit of symbol shock at times. I went in knowing little about AI and came out knowing a bunch of algorithms but not to much on how to put it all together. I can read the instructions myself. Knowledge of numpy and some basic knowledge of ML via a course such as ML4T would also be beneficial. Listen to the people who tell you that you need to be a master of probability or else you will fail. There are two exams and six assignments, but you only use your top five assignment scores. However, the median would most times end up being a 100%. The midterm took me around 40 hours and the final around 60 hours. Again, I was lucky to still get an A since I have good assignment scores and extra credits and A covers more than half of the class, but still I think the cross-checking partial answers should serve the purpose of guarding vs minor mis-calculations, especially when the numerical questions have been clarified. Just save yourself the money. Tips: Start early on assignments and even earlier on exams. This is probably because the course was quite new at the time, The TA and teachers are there to answer your questions and were active, Some projects are a LOT of work I spent weekends on it, The material is very well done but not very funny to watch even if they tried their best Other problems: The professor was active and involved in the course, recording regular challenge question solutions to emphasize important course concepts. This was my 3rd class at OSMCS, and Im expecting to receive an A. Lectures: I really enjoyed the lectures. Unnecessarily tedious take home exams, but other than that class is interesting and not too hard. I spent about 40 hours on the final exam because of this nuisance. This concept of a code review or individual one-on-one office hours were completely unheard in my previous class. Everyones background and strengths differ, so whats challenging to one person may not correlate with another. So, our head instructor was one of Dr. Starners Ph. Overall, its a good course if you have any interest in AI. The autograder (i. e., Bonnie) used to grade assignments would get overloaded the weekend that assignments were due and cause all kinds of reliability problems. Some of the questions are fun and they feel like teaching more than testing. Go into this class with good probability and python skills. Working on assignments was a big help to further digest what I learned. The lectures do sometimes skim the presented material, but are structured well to present the basics. Notable examples are the EM algos on lab #5 and the backpropogation Useful tips i have are: Overall fantastic course. This class seems like it would be very difficult for someone new to programming. For the logistic/execution this class falls short, 3 stars. This is my 2nd OMSCS class, my first being 7642. 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. Overall nothing too bad but was annoying since youre already stressed out, The stress of your grade till the very end of the class. The professor is cool tho and there are office hours by the TAs. The one problem I had was with Bonnie, the homework grading system. Like I said in my advice, if youre interested in the topic and you feel you have the prerequisites covered, absolutely go for it! You should have working knowledge of college level mathematics such as calculus, probability, and linear algebra. All the grading is automated, so they really only occasionally clarify things on piazza. But no matter how many hours I spent on the assignments, I couldnt get everything to work correctly. The 4th is definitely a more relevant edition. You will get a chance to learn that material mid exam, which last for a week. Having the lecturers involved in the course is rare, and pretty awesome judging from the other courses Ive taken. This is an interesting class with a good textbook and generally well thought out projects. A 60 page final is enough work. Thad and the TAs were excellent in every way. On project 3 (Bayes Networks), I only got to 85 after 37 hours and 20 submissions. A couple of the questions were poorly worded and caused many to lose points unnecessarily, but the TAs seemed to do their best to rectify that through either clarifications or by just giving points for certain questions. book When they move, the space they previously occupied is blocked, so you cannot move through it or move to it again. . The difficulty and workload reviews I see on this site were way above what I experienced. However, if I had to go back in time, Im not sure if I would want to put myself through it again. It would also allow for the midterm and final to focus on topics that have been fully taught. These students, sorry to say, have no business passing this course, but they probably will due to lenient grading. The lectures arent bad but I want to stress the reading because I know some courses in the program have reviews saying the textbook doesnt align well with content, etc. The final exam was medium difficulty and midterm was easy. 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. Also, if you do not know how to code well you will struggle. So, P(AB)=P(A)P(A|B) = P(A) but P(AB,C)P(AC)P(A|B, C) \neq P(A|C). {5} Assignments become easier after the second one. You get exposure to so many concepts which could be their own (and some are) courses, so its really fun to learn at least a high level understanding of so many core AI concepts. and pls dont trust others saying on this forum that only the first two proj are hard. The course schedule and weekly announcement clearly outline what is expected of you to do every week. Most projects have seemingly arbitrary Gradescope limitations (only 3 submissions every 6 hours, 2 every 60 minutes, etc. I kinda wish they had made each project smaller yet have more projects to cover all the topics we studied. The book: You are expected to read this whole gargantuan book. I am still waiting on grades - but assignments are highest weighed (60%) - except for 1-2 , you should be able to get 100 on them (Piazza and TAs help around a lot) , so even if you do fairly okay in the tests a B is easy achievable. Very hard, but very interesting. I am someone that is very independent with my learning and seldom will reach out for help, so my analysis may be skewed here, but I have zero complaints about the support and teaching staff for this course. One of a few professors that has real office hours (both as a group and individual breakouts). I wanted to do them, but there was absolutely no time for me. Strong Python but no prior CS experience before this program. The lectures are disorganized and are a mashup of videos from a handful of lecturers, making it confusing to follow. I read this was one of the hardest classes before taking it. Definitely read the chapter 13 & 14, probability and bayes net (BN Representation) before semester begins. Lots of work. You can wait up to a week and still wont get your question answered. Make sure you read through the prereqs on the OMSCS site, most of the I wish I had learned this before the class started complaints I heard were almost verbatim from the prereq list on OMSCS. I wish I had a better foundation in probability concepts before taking this course. I just wish the lecture assignments and tests had more overlap. Some weeks it is nothing. RIP. The knowledge you will gain in this course will not be shallow as is usually feared from courses that have a very wide course. Exams are disasters. I liked Thads approach to designing assignment and exams, particularly the aspect that assignment start with topics covered in lectures but then gradually goes beyond so that you have to research more advanced topic. Dont let the first assignment scare you away. Best class Ive taken so far (out of 4). The exams are open book, but are brutal. This was my first class in OMSCS and I thought it was fantastic. Let me clarify. I think the format of the exam was much better for teaching class concepts than the traditional 2-hour exam block. Even though some complained, I think the overall sentiment for the exam was very positive and along the lines of: Even though that was crazy difficult and tedious, I certainly learned way more than a normal test and am glad I made it through that. 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. All in all, be prepared to teach yourself difficult concepts with little help, unless you find some nice classmates that are willing to helpout. There were also some people from corona-hit countries expressing their concern about their countrys situation and personal situations during the course in slack and not even a simple annoucement of concern was sent. If you dont need that bridge, save the time and go straight to the sources. Stay active on Slack or Piazza because youll get through this together. Project 2: Exams: So if you are gifted or a genius, this review is not for you. Pros: {1} You will learn a lot. Lots of theoretical contents, insufficient local tests and just 5 Gradescope submissions. Do not underestimate the exams because they are take home - they are no joke. Get the AIMA book, its better than the lectures most of the time. Better yet, do it both ways to check yourself. If you dont have a strong grasp of probability and a pretty good handle on linear algebra, youre going to have a bad time. Lecture videos for this course make a lot of advanced topics very approachable, and I felt like the assignments lined up nicely with the assigned lectures and readings.

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