I found it much more challenging than the midterm and I believe this was due to the lack of relevance to the projects / lectures. There are 6 assignments and the best 5 are counted towards the grade. Thruns videos covered some of the hardest topics (e. g., Bayes Networks), so they were a big disappointment. not wait 3 hours before you can test again (they limited 3 submit in every 12 hrs, or something similar). TA interactions are great. Part 1 you will build a one dimensional model, recognizing words based only on a series of right-hand Y coordinates; in tl;dr : 6 VERY difficult projects, one every 2 weeks. If you can survive the first eight weeks of the course, youre going to be ok. Great course overall and a lot of interesting materials are covered. Mean 53.600 37.168 74.176 Took this the 1st semester is was offered, so it might have changed. Assignments are graded with GradeScope, which provides instant feedback on your score for the assignment. Exam 1 was awful. Im finishing the semester with a very solid A. assignments The lectures are lackluster and the exam experience is horrendous. exams I dont feel Ive learnt anything after I spent tens of hours on those projects. IMO, not helpful. Content-wise, the course is basically a survey of some basic AI and ML algorithms, you may actually have heard of implemented some of them in other classes, but still, it gives you an wholistic view of whats in your toolbox, and sometimes they framed it in a way that uses different algorithms to solve the same problem. I felt this exam was more challenging (on average) than the Midterm mostly due to challenging Bayes Net & Logic questions. There was always something that I should be working on throughout the semester and that was exhausting at times, to the point that I regrettably skipped the final assignment to catch up on learning materials to prepare for the final. Yeah, they are 30 something pages long, but most of them are instructions and its open book. It is frustrating that code provided to test the assignments is broken and TA iterate the assignment multiple times . if youre going to take this course make sure you understand probability (especially Bayesian), be very comfortable with Python, and have a good working knowledge of Numpy and dealing with large numpy arrays. Youll have a much better time in this class if you just read/understand/follow the directions. For the online section, A ended up being about 87%, and a B was about 74% . Exams are hard! I highly recommend everyone to take this course as you are sure to learn something. The professor and the TAs in this course were bar none, the most responsive Ive seen. Topics are super interesting and important. 1: Start early, if possible as soon as the course starts. I had read many reviews that scared me and almost made me withdraw from the class before even starting. See if you can do any of those assignments. There were numerous clarifications for each exam, even up till the last few days of the exam. Tough luck kidos, you are in GT. Make sure you are caught up with the material (or at least most of it) before taking them. It could be overwhelming for some as you will be pressed for time. Final (no midterm since it is summer): There are definitely some bright spots in the course and I could see this being a strong offering later on if they refine/replace a lot of the older and poorly quality controlled content. The medians for 4/6 of the homeworks were 100s, so your grade is entirely dependent on how you do on the first two homeworks, the midterm and the final. Spring/Fall, the midterm and final are 15% and 20% of your grade, respectively. I took the course without much preparation and even though I did well in my mid-term and final (above median), I struggled with Numpy which costed me dearly in two of the assignments. I took this course during the during my second semester in the program along with CS 6035 IIS. Strong Python but no prior CS experience before this program. It is important to try your best to get the extra credits, some of them are actually easier than you think. It was a great first class for someone who was still relatively new to core computer science concepts, but was fairly fluent in math and statistics. This was a nice change in pace from coding endlessly. This class is worth it if you have the time. Id brush up on your statistics and matrix math as well (or at least be able to decode matrix math equations). It was a lot of work, but I learned a ton. The grading seemed to cause some stress, since its based on the median and standard deviation, but rest assured that above a 90% is an A and above an 80% is at least a B. Unless you have a lot of time on your hands I would not recommend taking this course as an elective. They are 40-50 pages long and with a mix of types of questions (MCQ, short answers, fill in blanks, etc). For anyone thats taking multiple classes or juggling a fulltime job, you probably wont have time to even try these. There is a fair amount of partial credit, just be careful and double-check your work, I got 90 in the midterm and 88 in the final and surprisingly found these assignments the less stressful of all of them. last year Due to static nature of the trellis values, local tests are extremely limited. 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. Gaussian mixture models (26 hours) - If youre not good with linear algebra or numpy then this project was brutal. You are here to learn interesting ideas! Hence, definitely schedule time to review your answers and not just answering them. (One assignment involving MCMC is covered in the lecture for only 90 seconds). Most parts of the class were polished and in combination provided one of the best learning experiences Ive had in the program. If what that review as far as their background goes is true, and they arent b)dont have a CS background, c) already work in CS/software field, then they are a) arent a genius, in which case, congrats to you, your review means nothing to the rest of us who arent geniuses. Whats worse is the cross-checking figures are once changed by clarfications, and the clarifications even changes the value iteration algorithm to slightly differs from the lecture video example so every value iteration algorithms in the course subtly differ, and I totally failed that question although I repeatedly tested my algorithm vs all course examples and I totally align with the cross-checking figures. I managed to get a 96% before the curve. All assignments can be completed with runtimes less than 30 seconds. Always prioritize coding efficiently. Looking back, this class has definitely made me a better programmer, and introduced me to some difficult graduate level algorithms. However, the median would most times end up being a 100%. While this is a hard, graduate class in AI from a top Institute, it is not impossible and you have many tools at your disposal to help you learn and succeed. Assignments 3-6 were all fairly easy. But its very doable, if you are willing to spend efforts and time. Would not recommend taking with another class if you work full time like me. Exams: take-home exams that take 10-20 hours to work through. One assignment I particularly enjoyed was decision trees. Those are the best tips I have read so far and would like to offer. State1 In terms of difficulty, I found the course to be fairly difficult. I went from A/B boderline to B/C borderline in one assignment. Found this format of exam very benefitial. This course is NOT the entry level course even though the contents are classical but outdated. Also a very difficult assignment. Highly recommended, much better than KBAI, just be prepared to work. Additionally, they will even hold code reviews for you and actually look at your code and give you pointers or ideas. (Im not saying I could do better, but there are better lectures in the OMS program at this point). Most projects have seemingly arbitrary Gradescope limitations (only 3 submissions every 6 hours, 2 every 60 minutes, etc. Feel really bad about it too! I had mixed positive and negative interactions with the TAs. The videos and text book are excellent. This led to some brute-force/blind debugging in some cases, which was a little frustrating. stop when you get over a 90 on A1 or A2 - pace yourself. There were complaints about absence of TAs, so Id suggest them hold daily mentoring sessions instead of just 3 times a week for summer terms (perhaps less frequent for spring/fall since its less intense). Writing my own tests to fill in the gaps was a great skill to get more experience with but I never complained when I was given all the tests I needed to complete the assignment more quickly. 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. Std You However, having so many lecturers it feels somewhat thrown together. Despite previous comments, I actually thought Piazza had pretty decent activity and got helpful info from there regularly. This class took some grit to get through. The final 3 assignments had very little to do with the final exam which was surprising to me. 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. As a note of context, this course was advertised to be offered in Spring 16 and then cancelled late Fall 15 and then surprisingly re-released at the 11th hour. Midterm was tough and the final was a whole different beast. However, especially when Thad was teaching, there were somewhat of a very light explanation of really complicated subject matter not well explained. You shouldnt be intimidated by this (it can be a little dense but a vague understanding of the logic should be enough since the exams are open book), but just an FYI in case you are only interested in practical skills. Out of 6 assignments, only the top 5 scores are used. There were 6 assignments in the Fall 2017 class; you get to drop the lowest grade. If a system has unobservable (hidden) states and each state is independent of the prior, then we can create a Thads level of interaction with the class on piazza and in the office hours really made you feel like he cared about your understanding. Projects involve implementing multiple algorithms like A*, RandomForest/DecisionTrees, HMM, etc. Start everything early and ask questions. The assignments were good, but the last parts would get challenging. All the grading is automated, so they really only occasionally clarify things on piazza. At this point, you will have two observed coordinates at each time step (frame) representing right hand & left hand Y positions. starting as early as possible is the key. I gained from these and stood on the shoulders of giants before me. However, if you are like me and feel uncomfortable not achieving 100/100 then prepare to spend dozens of hours in this assignment. notebook.ipynb: Optional Jupyter notebook to complete the assignment. They created challenging but rewarding projects and were very responsive to questions on Piazza. The assignments are very hard and take lots of time, and require very good knowledge of Python. The exams are difficult, but fair. In the autograder, we will also test your code against other evidence_vectors . This was certainly the most rewarding and difficult part of the class! Sometimes the staff would acknowledge the issue and make a correction, and other times not. Overall, I struggled with trying to fit the assignments to what is expected in gradescope, which really leads to me never truly understanding the content; this becomes a problem when the tests come around. I initially put in way more than 20 hours/week during the first assignment until I figured out how to get help from the TAs using Piazza. All the topics are covered in a depth that you can get a fairly good idea about the topics. Thankfully, we got to drop our lowest project grade. The difficulty and workload reviews I see on this site were way above what I experienced. I think this was very considerate of the teaching staff to front-load the material so that you can withdraw if it turns out youre unprepared for the course. All that being said, the TAs were mostly pretty good and were very smart and helpful. Firstly, the book that is required for this class is AI: A Modern Approach, and is by far the densest textbook that I have read. Understanding recursion is a must - two labs use it extensively. The course provided a good overview of the fundamental AI techniques such as search, Bayes networks, HMMs, etc. I mostly used it as reference to do assignments. Even if it was briefly covered in the lecture/book, it will be there on the exam. Some weeks it is nothing. I can tell every assignment was challenging and required a significant amount of effort to complete. Doubled up on this class with Network Science. 3: Not so much code involved, but I would say that it is harder than A1 and A2. Previous experience with machine learning, Recent experience with college level mathematics, Both very hard and and very time consuming, the lessons introduce the concepts, the reading fills in the gaps, and the projects and exams are where the reinforcement learning really takes place, Do ALL the reading. If you are looking for an easy class for some reason and just want to get by without providing much effort, this course might be more challenging. The instructions for the assignments can be a bit vague at times but you end up learning quite a bit of material from them. If you have time, get familiar with numpy, Brush up on linear algebra, logarithmic math, and Big O notation, The content is very interesting and we implement ourselves a lot of algorithms, Lots of problems with Bonnie and the automatic notation was not always fair. The projects were interesting and helped me understand a certain topic very well. Nonetheless, I still struggled significantly, especially for the first two assignment which took anywhere between 20 - 40 hours for most people. Thads lectures are pretty good for the most part. A few of the sections use videos from the older intro to AI course but I think those will be updated. The final.man. This class is very interesting though and I liked it even though it was painful. The exams are super hard, take-home, but overall it is a really fulfilling class. The midterm and final are open book, and you have a week for each. Gradescope: The other class members (and some TAs) were quite accomodating on Piazza. A quick recap on search. I learned lots, the lectures are fun and the assignments are interesting. You decide. Useful for general understanding, but overall lacking in substance. This course is very hard. A* search achieves better performance by using heuristics.
This course was the initial offering of 6601 for the OMSCS program. I had never coded in Python before this class that by the first two weeks, you better be ready to learn as you will already be coding and completing and application in that period of time. There is also probably a little cheating, working in groups, having access to friends that took it last semester so you can review their assignments. Provide the transition and prior probabilities as well as the emission I enjoyed this class but the exams didnt really test anything other than how good you are at guessing. I did not score 100 but half of the problems are presented in a way where it is very difficult to screw up. Condolences to any future semesters if this assignment becomes required. Piazza was a bit dead as compared to my prior courses which is fine, just something to consider if you rely heavily on forums. If you miss a week, forget A, if you miss three weeks, consider dropping the course. Tips based on this semesters experience: Tough, but fair. The exams are open book, but are brutal. Could be paired with other balanced or easy course. And one needs all the time possible for assignments. Anything over 90% is an A. Dynamic Time Warping is a time-series classification technique which measures similarity between two sequences that can vary in speed. Exams were really tough. After taking it, I feel its actually not that bad. Dr. Starner is not very present in this class outside of the lectures. The exam covers everything from the lectures and I felt was very fair. Both were week-long take home finals that were open note/lecture (not internet). They use the median to determine your letter grade: A = if you are above min(90%, median(final grades)). Hopefully those videos are updated at some point. Prof Starner cares about the experience of online students and it shows, both in his posts and the culture amongst the TAs. He has been very active on Piazza, and the TAs are all excellent. For people debating between Spring-AI and Summer/Fall - AI, pick anything other than Spring. Thus, when the opportunity came to implement decision trees from scratch using only Numpy, I relished it. The first two are practically impossible and take over your life. They were probably the most difficult piece of this course (for me). I was genuinely excited to take this class, put in a lot of time into it. I think true learning happens when you are at least somewhat challenged and put outside your comfort zone, i.e. I dont do well with the cram everything in your brain for a test approach. I thought this course was not hard even though it was offered in the summer for the first time. The TAs did a fantastic job of organizing assignments, responding to questions, and providing students with good local tests. 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. But if your implementation runs afoul of the grader (even if it works locally and passes tests), get ready for many hours of hair-pulling trying to figure-out the inner workings of the black box that is the grader. Be prepared. This class does have a lot of room for additional exploration and deeper diving into the topics, sometimes through extra credit, so there is that benefit if you take it by itself and limit your non-OMSCS activities. There isnt anything. Feedback Lectures were a little lacking in detail with a few exceptions. Requires python programming. In the end, an overall grade of 69 and above was a B, 85+ an A. Grading was fair, students need to chill. My background: OMSA student 7th course, c-track (other course is RL). I spent about 40 hours working on it and could not get it to pass Gradescope, even though local tests were passing. I later realized what I wanted was more under the umbrella of machine learning or reinforcement learning, but alas! It teaches you the advanced concepts of AI ,and be able to apply them. There wasnt much math involved and we were walked step by step through the process on how to build each algorithm and how each algorithm was a progression of the previous. Project 1: They both felt like problem sets aimed at helping your understanding on the topics. But also I think its extremely overrated. Lecture videos were okay. If possible, try to take it solo. This class is rough. With a full-time job, married life, and the everyday stresses of maintaining health and sanity, this one course made me lose more hours of sleep than I was comfortable with and it was my only course this semester. I actually enjoyed A1 but A2 was a nightmare. The timing could not have been better. I thought the worst part of the class, as many people have already mentioned, is the lecture videos. There were too many moments of utter confusion with nowhere to turn for an answer. Objects were still segmented by color, but additional coloring replaced the original shade to provide more contrast. C is a building being crashed. I found the TA answers to questions mostly unhelpful. Every question in the exam has typo and errors that requires you to go to Piazza and check for Errata post which is constantly being updated (even before the night which the exam was due). Comfort with mathematical notation will help you, but deciphering equations and re-writing them as code is different than actually doing math (imo). doesnt matter. Sometimes someone else explained it quite good, why reinvent the wheel and like this design in the lectures. 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. 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. Some of the lectures felt more like a refresher than trying to teach the material like new. This is definitely a no pain no gain type of class and I can honestly say that I know far more about the field of AI, including ML, than I did before. Especially the final. This class was pretty fun. The course is really math heavy and doing well without too much stress on the assignments requires a lot of numpy and python experience. Each exam question is an extreme deep dive into one of the many subjects this course covers then modifying one small feature of the subject and asking vague questions about it that you must infer a lot of what the TA was thinking when they wrote it. TL;DR: You must be a very good with Python before taking. On project 3 (Bayes Networks), I only got to 85 after 37 hours and 20 submissions. I felt that they were grading as much on, can you learn this quickly yourself. The questions are not hard and instructors were always there to clarify any possible ambiguity. he led the data science teams at Lazada (acquired by Alibaba) and uCare.ai.

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