98 or 99 in many cases. observations. You have two weekends for each assignmentyou should definitely start on the first weekend! 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. 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. But be prepared to put in a lot of hours to get a good grade. As for the review above mine, it is extremely overdramatic. This was the only course I took this semester. Its a great survey class as well for machine learning and AI in general. I took this class to get some exposure to ML/AI and to see if Im interested in pursuing more classes in the domain. Its not easy class and requires lots of time devoted, but the course is very well structured to ensure you obtain knowledge after each assignment. Consider that carefully. Not hard, but can be tedious. The material is very useful and interesting. But if you are weak in those areas, then I recommend taking another course before this one or a refresher before the class starts. However there is tons of content throughout the course and textbook if you really want to get into the weeds and keep busy. The final was a weeklong take home exam, which was extremely tedious and prone to error because it required crunching numbers by hand. You dont need to be a python guru to do well in the class, but you should be comfortable programming in it. ?), opening the course with adversarial search instead of with actual search algos, and many other small issues. Another one of those drinking from a firehose classes .. You will know in the first two-three weeks whether you will enjoy the challenge or be defeated by it; however, I will say that if you stick the course, it will be very rewarding. Most other assignments have a ~100% median. I learned more from these exams than I have ever learned from an exam in the past. Exams are hard! During Final week you HAVE to be watching Piazza regularly for updates. I managed to get a 96% before the curve. Towards the end of the class I started falling behind on the readings. The opinion of others will differ from my own, but make sure you have the time to commit to this class. However, with enough effort, it is more straightforward to achieve full marks with these (but dont start too late!). The material was super interesting. Thats all the info you need. Gaussian mixture models (26 hours) - If youre not good with linear algebra or numpy then this project was brutal. You might find lectures a bit too shallow but textbook and additional readings make up for that. init Identify which part of the material you will need to solve them. One of the best courses out there. I had to use PTO to ensure I could finish in time. 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. If one has less programming background, consider preparing by learning Python/Numpy, a bit of search algorithms and probability before starting the course. Many dropped out around the first midterm, and the remaining students were rather remarkable as a whole. What you get on Gradescope is your score which makes life a little less stressful. 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. If we know don't know if a patient has cancer (event C), knowing the result to test A informs our estimate of cancer, which in turn informs our estimate to the test B diagnosis result. The final was pretty tough, but definitely fair game for a grad level AI exam. It has the potential to be a very enjoyable class, and the difficulty of the assignments didnt lead me to having a stronger grasp on the subject. During the summer there were five assignments, with the lowest score dropped, and only a Final, no midterm. 0.1 stays 0.1 or 0.100 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.. Dont want to insist too much, but various other things added to frustration: fighting Bonnie (with its arcane errors) for assignments, fighting with TAs about solutions for mid/end term exams, the grading on the curve (is it enough a 90% to get an A, when most assignments have 100% as median?? Full Document, International Project Management Association. Eventually, the nuances of these algorithms will stick. Also if youve taken ML or RL theres some overlap. BUY Peers discuss more freely on slack, so that is where the only help has been. Notable examples are the EM algos on lab #5 and the backpropogation You dont need to be good at math for this class. The lectures were not very deep or engaging. When you take this class, those other concerns get put on hold. Even the professor remarked that the challenging questions threads had no activity. I thought the worst part of the class, as many people have already mentioned, is the lecture videos. I liked the course a lot and learned a ton, just dont expect to get your hand held. The exams are not to be underestimated. They each took about 10 hours to complete and motivated you to consult the book/lectures to fully understand the topics before committing to an answer. Note: Sample syllabi are provided for informational purposes only. is closer to State 2, and then you realize that 45 is still closer to State 2. Never had any correspondence from Thad Starner at any level. This course requires that one reasons from first-principles, rather than the, let me google for the answer on stack overflow approach so common in industry today. 4) The biweekly assignments can be interesting. This gave me a wonderful view of the world through Bayesian probability, a lot of confidence choosing/implementing AI/ML methods, and my first gray hairs. Here are the reasons which you should not take this course: Video lectures are really bad. That defeats the purpose of AI in trying to win. Gradescope: Each test took me about 30 hours to complete. Stay active on Slack or Piazza because youll get through this together. Assignments: Start early studying the requirements for these. I found the exams to be quite easy? Welcome gift: A 5-day email course on How to be an Effective Data Scientist . Some had restrictions on number of auto-grader attempts, but these specific assignments (markov models is one) were very easy. Students were only allowed to refer to the lectures, book, and Piazza. The problem was that these questions take a massive amount of work to complete and you have to perform some tedious calculations to get your answers where some small mistake can cause a cascade of errors. 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). It was a challenge, and I honestly would not recommend it during the summer. The highlight of this course is the 2 exams which are open book for a week. About half of the assignments are really great. Its difficult but once you get comfortable with the codebase it gets easy and you can start reading and getting ideas about how to improve your agent from the book. Mapping the pages and chapters was nontrivial since the 4th edition was completely re-organized and revised. is two handed, using observations from both the right and left hands as features can increase the accuracy of our model None of them posed a level of challenge comparable with assignment 1, which was disappointing as I felt I wasnt learning enough. Had I understood all the mathematical syntax or read an additional 75 pages from the book then maybe I could have gotten it but ran out of time. The instructor and the TAs were all awesome and very helpful! I thought the book was very good, but we only really dived deep in a few areas. It is a nice format, but some pretty crazy questions that were tough to answer even with a book! {5} Curve was close to regular 90/80/70. I never went to office hours, but they were hosted regularly as well. Definitely the best course I have taken so far. As the course advertises in its description, it is intended for those who have some background in AI or are ready to jump into the deep end (true statement) The description asks for 9 hrs per week and it should be modified to reflect the true effort required in this class. 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. writes & speaks Come on guy. A new exam seem to be developed every term, and thus they were less polished than expected. Learned a lot, this course focuses on the foundations of how models like decision tree, Bayes Nets work. Overall they were interesting and helped me consolidate the concepts learned. This course is a very good course to go over AI in general. My lessons learned and advice is to complete the lectures right away to let the concepts sink in and start the assignments early. Midterm and Final exams will be really long but wont be very hard, with the help of extra score and a nearly prefect midterm(around 99), I only need around 60 in final to get an A. My impression is that Dr. Starner seemed to care catching plagiarism than actually helping students solving the problem. You are only given a week to complete and they are given concurrently with assignments. The textbook provides great pseudocode which you are instructed to use for the assignments.That and the supplemental resources the teaching team give you are all you need to complete the assignments. However, it pleasantly surprised me and I would recommend this to anyone that is an independent learner and is passionate about learning AI. People dont even do CIOS and you expect us to rush this extra credit in less than a week? Note, the median is done at the end of class so the test bring down the medians to a nicer range, but it can be quite frustrating to learn that your 95% or 90% on a project is a B. The first assignment was easily the hardest in the class- it involved designing an AI to defeat a 2 queen board game. A fun but challenging course. I wish there was less focus on the math and more on the ideas which is what I thought a survey class would do. You will get a chance to learn that material mid exam, which last for a week. I saw messages on Slack from students that were trying to debug their implementation using print statements and, at that point, I felt sorry for them and didnt think they could survive this course. We also implement expectation maximization to utilize GMMS for this same problem, which results in a similar output image. The result is that you have to flip back and forth between the corrections thread updates. The material is challenging but fair and often fun. There are tons of extra credit opportunities, and the worst project is dropped. 7 days to complete each exam is difficult. This is my first semester into the program and Im glad that I had a pleasant experience. Background: CS Degree from a top school, working as a software engineer. The midterm and final exams were take-home, and they were good learning tools. last year The best instruction came from the guest lecturer Sebastian Thrun. Professor Starner was fairly involved in the class and answering students questions which made the class more lovely and desirable. They did require effort, of course. The first two assignments were by far the most challenging (and my lowest scores), but after that the class felt much smoother. My FT job is very flexible, which helps, but my PT job is very much the opposite. tl;dr : 6 VERY difficult projects, one every 2 weeks. 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. You actually program the AI projects that you learn about in lectures. This was my first class at GT OMSCS and I would recomend it as such. Fantastic assignments. Interesting & short assignment burdened by overcomplicated & broken rounding rules. Like many other courses, it covers a multitude of topics at a shallower depth, rather than covering fewer topics at a more advanced level. The exams for this class are a complete joke because its basically just the TAs getting together to figure out gotcha questions. This class may be easier for you if you have already taken an ML class or are good at debugging algorithms. Most questions required programming. The midterm and final exam are take-home exam, and are also time-consuming. NOTE: Submit your pdf with what you did to get to the answer. This is my 4th course & clearly Ive spent the most number of sleepless nights(almost) during this term :) Dont be lulled into thinking only the first two are tough. You should have completed undergraduate computer algorithm and data structures courses that cover O notation, time and space constraints. The lectures do sometimes skim the presented material, but are structured well to present the basics. Project 2 - Graph Search, Djikstras, A* - good lab, and straight forward. Definitely belongs where it is among the top 5 hardest OMSCS classes. 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. In the autograder, we will also test your code against other evidence_vectors . Give it another couple semesters to work out the bugs before taking this one. squeezed out by an adjacent state; that is, a state might have its only observation moved to another state. Given this information, we can model each state for each word by their sequence statistics and apply this model to estimate the base word for sequences we have not seen. I liked some a lot more than others, but all were great learning experiences. If possible, try to take it solo. Start early if you can and dont hesitate to message the TAs. Even though im only through 3 projects and havent done the mid-term yet I wanted to give my review for those considering the class for Summer or Fall especially after seeing some reviews that I felt were a bit dramatic. Ive enjoyed the class (aside from the rough start on project 1) and have learned quite a bit. Most problems probably due to first time offering. Dont get too intimated by the threads you see here. Its got a ton of information and some of the algorithms are broken down well. We were only graded on the top 5 of the 6 assigned homeworks, which would be very convenient should you need to miss a couple weeks due to life issues or something. There was no midterm, but there was a week long final. I mean, come on, they did give us practice exams with solutions prior to the final. Hence, I come into the course with a fair bit of background knowledge. Thats why were watching the videos, Guess at an answer thats either intuitively easy or so hard you have no idea, 5 second video saying heres the answer, with no explanation, Isolation (MinMax, Alpha-Beta w/ Pruning, http://primaryobjects.github.io/isolation/), Decision Trees (Fitting and classifying data, random forests), Hidden Markov Models (Didnt attempt, just took this one as dropped). Overal I loved this course and I think it was a great first course to AI before taking more ML related courses, as long as youre ready for the challenge. This means you cant use easier to follow explanations of an algorithm on spots like Wikipedia you had to use the confusing books explanations. This is indeed a hard course and has quite heavy workload but definitely doable. 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. I turned in the midterm and final not knowing if I scored a 10 or an 80. I am sitting between a B and a C. If I blow the final, we will see how low the curve goes. For those that do not want to read a very long review, the next section are the highlights. If thats you, GT thanks you for your donation, see you next semester when you withdraw and try again. The projects are well designed and I learned a lot through them. I think having tests that take 40 hours isnt meaningful. Modify the Viterbi trellis function to allow multiple observed values (Y location of right and left hands) for a state. There are 6 very well design assignments. The exams are open book, but are brutal. They would be very hard if you werent reasonably good in python, otherwise they were tough more for the last 25% of the score. Some hand-holding as well. The exam covers everything from the lectures and I felt was very fair. . positions of the left hand (Red), right hand (Blue), and nose (Green) change over time in video number #66. CONGRATULATIONS! The lack of communication was a recurring theme, culminating in the frustration over the final exam. If I had more time I would have liked to read all the material but given that the summer class is shorter with the same amount of content covered in the class, and that I was travelling during some of the class I didnt have time to do everything. Take lots of notes as no internet usage is allowed except the notes you have an any links they have in them, Review AI topics beforehand (use google to find the syllabus), Taking ML first would naturally make the ML portions easier. The final exam has a chance to hit you blindsided if you havent been reading the book and keeping up with lectures. (limited to course material) so theres nothing to memorize before the exam. It covers a wide variety of topics in great depth so be prepared to put in the time. As far as assignments go, theyre all hard. The first two were pretty brutal, and I walked away with a grade in the mid-90s. Start the assignments just about as soon as you get them, and dont be shy to post questions to Piazza. 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. Whatever you do dont try diminish the experience of being lost by asking for clarification. Now, we can take this knowledge and apply it to any Bayes net configuration! There were no class-wide cheating scandals in this course, at least any that were reported. The course carries quite a lot of self-engagement opportunities in terms of learning deeper. Exams were heavy on calculations. The video lectures are well made but sort of sparse, and some additional reading is required to get a strong grasp of the concepts. if you want to get some good start, Id recommend at least to finish up the reading for first few weeks, so you can use those room for assignments. JOHN BUY CAR [FUTURE] Part 1a: Encoding the HMM [15 Points] Brush up on probability before taking the class. They use an autograder system called Bonnie. Overall, its a good course if you have any interest in AI. I would recommend professors balance out the class by decreasing the assignment load and allowing for us to spend more time digesting the material. But amount of topics covered is enormous and everything must be understood to the last detail, otherwise its impossible to do the assignments and the exams. I learned lots, the lectures are fun and the assignments are interesting. The first 2-3 projects being the hardest of the bunch. 47, 39, 32 34, 36, 42 42, 42, 34, 25 It was a test designed to make you learn what was not covered in homework. The midterm was easier, in my opinion, whereas the final required more going back through the book because it covered less content discussed directly in lecture videos. 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. However, the projects focus on reinventing the wheel topics like Minimax, Alpha-Beta, Decision Trees, etc. Youre just helping the curve. Learn Numpy and it will be used heavily in later part of assignment. This is a great course that gives an overview of all AI/ML techniques. The remainder of the projects were less coding heavy, but involved understanding more theory and math, which keep the workload challenging and rigorous for me. In my opinion, the book and lecture material is not that useful after the first two assignments and becomes increasingly disconnected from the projects as the class goes on. The midterm felt like an extension of the projects with a couple sections with non-project related content that held your hand pretty well. They also give you a week to complete the exam so theres no real studying for it and you should expect to spend 10-15 hours on it. Learn Git and GitHub without any. The assignments are well organized and make it clear what parts to start first (coming from ML4T, this was a relief). Some of the questions are fun and they feel like teaching more than testing. Unless youve got a 100 on five projects, dont think that you can skip one. I suspect that many in the class are just that smart - bordering genius. This really expanded my skills and learnt a lot! Thats ridiculous. The following diagram shows how the 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. If you can manage to complete it in one week, youll get every other week off. Instructors and TAs good. No big deal at all. The assignments are intense but overall manageable if you start early and use the resources available (I found the slack community the most helpful followed by piazza). I think this is a great first course for those doing the ML specialization, even though its not technically an ML track elective, because theres an introduction to machine learning and reinforcement learning in both the lectures and the recommended textbook. Why this class is hard: Thats pretty simple, complexity of this class comes out of 3 things: So in order to expect an A in this class youre expected to be able to self-learn or already possess required knowledge, be good at math and coding. 3: Not so much code involved, but I would say that it is harder than A1 and A2. Those are the best tips I have read so far and would like to offer. Are you familiar with the basic concepts of linear algebra, probability, and single/multi-variable calculus. This was my 7th class, and I have taken RAIT, ML4T, KBAI which may relate a little to this class. For the final, if you can code an answer, do that so that you can change the input values when they are inevitably going to updated in the finals clarification thread. I think that if I were to take this course I wouldnt do so unless I had studied a decent amount of the material ahead of time as you will be pressed with both knowing the material and demonstrating that knowledge in python. We can go even further with tri-directional search. The lectures dont really go into details some of the times, you need to read AI a modern approach 3rd edition. . Could be paired with other balanced or easy course. If we use an admissible heuristic, we are guaranteed to find an optimal solution. Welcome to my shared space of projects in engineering, startups, and personal interests. The course material will at time feel overwhelming. Assignments: There were 6 assignments with the grade composed of your 5 highest homework grades. Then the clarifications needed clarification, and so on. The game ends when a player can no longer make any moves, and they lose. All told, I averaged about 10 hours per assignment on the last five assignments, and spent roughly 20 hours on search, and have been at or above the median on all assignments. This was my last course in the program having taken: CCA, ML, RL, CV, AI4R, DVA. Exam 1 was awful. 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. This is certainly an opportunity to become familiar with the AI field with an intensive, hands-on experience from which you will benefit greatly. I actually enjoyed A1 but A2 was a nightmare. He hosted office hours before each exam and that was it. After assignment 2 my excitement began to wane. Here are some thoughts: 1) This course really should be split into 2 courses. Please read https://omscentral.com/review/-Mg2meE3KVrVBVS4yIDu and DONT TAKE THIS course if your answer to any of the categories under The type of student I think would struggle is YES. Fellow Classmates: youre never going to look at it again anyway. Slack and piazza are your friends. Any course that regularly requires 30+ hour weeks can put stress on your job, and your marriage. No need to calculate eigenvectors by hand, but be familiar with simple matrix operations (products, transposes, inversions). The exams more or less test if you can apply concepts learned to something you havent seen before. Overall, I can say that I learned a lot from the course, its not impossible, but it can be executed much better to allow for us to retain the maximum amount of knowledge while still giving us practical experience in the assignments with some AI problems. The course was taught together with the on-campus course, so the Professor was extremely active, and the TA staff was much better than usual. there are 800 people on here. Taking this course during the summer seems like a bad move, because there is no midterm. The other projects were not as bad but that is relative. I would highly recommend this class, and if you have a similar background its not as arduous as its made out to be. Its not like they demonstrated this passion once in a while but Ive noticed the trend consistently through out the term; theyve gone out of the way to explain things be-it assignments that each TA handled or holding office hours, answering questions on piazza etc. This is my 7th course in the program, and I work full-time. the first part is the hardest and gets easy after. 5x the time. One other thing I found helpful from this class was the exposure to writing our own tests. . What is the probability that the squad will have, A text file words.txt is given, which contains several words, one per each line. Exams: Oh boy! The first two were much more time consuming than the last four. Overall a really great course to survey many of the AI topics, sometimes it felt to me they are rushing through the topics. You may have to rework some problems based on revisions. Challenging in the good way, meaning the material is intellectual and the algorithms complex. Again though, for this project, we are walked step by step and are shown how to code the algorithm and how each algorithm progresses from the previous algorithm. Constantly asking questions to clarify the ambiguous wording. For example for the initial eval function testing board state, what should it return? Sabastian presented a few chapters (same professor from CS-7638) which I very much enjoyed. You do all work by yourself, not worth the money at all, better to take open courses elsewhere if - will get the same level of knowledge and help but for free. Overall, this course was by far my favorite while also being the most difficult and most work thus far. I took 3 classes this semester: AI, NSec, and ML. The material can be math heavy. State 3 has mean=70 & std=8 Recommended. You will need solid stats and linear algebra, and then you may have an easier time in the latter half of the course. There is a good class hiding somewhere in the course materials, but it wasnt on display in Fall 16.

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