omscs 6601 assignment 1

Quite tedious if you ask me. Another guest lecturer is Sebastian Thrun, founder of Udacity and Google Xs self-driving car program. 1. 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. Grading is curved and fair. I have been a software engineer for years, so writing code and debugging is just another day to me. The material was very interesting, and overall worth the difficulty. Overall, there are 10+ hours of lectures per week, ~100 pages from the text book per week, challenge questions every week to review on Piazza (not graded), and projects due every 2 weeks. I took one day off of work for the midterm and worked 12 hours 3 days in a row and was able to get an 89 and felt confident but exhausted. Along with the book and lectures there is additional material usually linked to from the weeks overview or some assignments, or by other students on Piazza. You get to drop your lowest assignment (they take the top N-1) assignments. 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. Subject itself is good. I found the book to be a necessity. Having the lecturers involved in the course is rare, and pretty awesome judging from the other courses Ive taken. The Professor and TAs are truly excellent. Tests will bring up obscure material from the readings that was never mentioned in the lectures. Overall, I am glad I took this course early in the program. There are those working on Ph.Ds in engineering, full-time students in the day program masters, and even professional data scientists taking this class. In a typical ASL recognition system, you observe the XY the projects are very interesting, but, unless you have experience in some related field, they will take a lot of your time. If thats you, GT thanks you for your donation, see you next semester when you withdraw and try again. Your questions are answered by TAs who may have completed this course 1 or 2 semesters earlier (but they answer quickly). (limited to course material) so theres nothing to memorize before the exam. Assignments take between 10-20 hours (assignments 1,2,5 were around 20 and assignments 3,4,6 were around 10 to 15) and you get about two weeks for each. The notion that was stated previously that they dont care is completely false and unfair to them and the effort they put in to our learning experience. This is what the TAs told us, verbatim from Piazza: You can use either. I also spent an amazing amount of time working on this and to basically just have to give up out of sheer exhaustion. The text is mostly a YMMV - but makes a great addition to your collection since it is a great book. On the down side - the course pacing wasnt the best. This means you cant use easier to follow explanations of an algorithm on spots like Wikipedia you had to use the confusing books explanations. 3) The exams (midterm and final) are long, but I did not find them as hard as they have been described here. The final was very lengthy, but difficulty wise, except one or two questions, it wasnt that super hard. I took this is one of my first classes in this program. It is much more focused on finding clever hacks to make a computer work faster and more efficiently to arrive at a difficult (to compute, but obvious to humans) answer. GovTech Conference - Data Science and AgileCan or Not? AI is a very tough course: intense workload and hard concepts. This is not to say that students should write additional unit tests - what Im saying is that the unit tests provided in the assignment were dangerous in that they generated a false sense of confidence. So you can spend more time learning than dealing with people. Lectures are only introductory. Unless youre a skilled programmer and used to watching lectures at 2x speed, you could easily end up needing twice as much. Overall I thought it was a really well structured class, for the material that it attempts to cover. Lots of theoretical contents, insufficient local tests and just 5 Gradescope submissions. I found this course to be extraordinarily challenging but also very well run and informative. Both fell way closer to the 80. There are 6 assignments out of which best of 5 are counted and one midterm and one final exam. I would recommend taking this with a less difficult class as the projects and material are very time consuming and complex. The rest of the class followed similar themes. Machine Learning for Trading involves learning about machine learning on sequential data, with lot of Numpy vectorization goodness. I dont even want to talk about that exams that are 40-60 pages and (very proudly) are announced to take 13-15 hours to complete! Go through the lectures/chapters first by the time assignment opens up, if possible else at least within 2 or 3 days of assignment start date. For some questions you had to do lots of tedious calculations that all build on each other, so if you get one wrong, youre loosing a lot of marks. I also found some of the wording and contradictions on the exam to be quite misleading (again, many others didnt have this issue). CS-6601 is a great introduction class to AI. I can read the instructions myself. I recommend not pairing it up with another course in a semester. In the finals answers threads, it was clear that some students didnt know basic stuff like how DFS work, or how to calculate conditional probability correctly. As long as you understand the concepts, the questions are simple but dont wait until the due date to start. I was curious about how artificial intelligence would be defined in a formal education syllabus. Took this course as my 2nd in OMS after DVA. The assignments were well designed and TAs are really responsive and wanting to help My first class in the program and I love it. You can game grading this way. Overall, I enjoyed the first half much more than the second half. Piazza engagement was essentially non-existent for the first half of the semester as Im sure there were other students that were scared off like me. Its true that the first two assignments are harder, but I wouldnt say that the rest of the assignments are a walk in the park. Its really hard and a lot of work, but its a very well done class. Not a huge deal to me but everything in the first half of the semester is valued more. No big deal at all. To reiterate, this class will teach you a lot, but you also may be blown away by some of the incompetence and disregard for students at the end. I have to say the TAs and Dr. Ploetz are some of the most active and helpful community I have had in a class. Finals official answers had too many errors. In most cases I waited for 24-48 hours before a TA addresses a question I posted on Piazza. I did not find that necessary, but did spend 30 hours total on it. Bad news is since this course trivially skims through topics, all youll come out with is some artificial intelligence and not real intelligence. (I got >90 on both) But the exams are riddled with typos, grammar mistakes, ambiguous problem definition, etc. part_1_a() Part 1b: Creating the Viterbi Trellis [40 Points] There are plenty of comments about the projects; theyre all hard, but the first 2 you will fight with a lot more than the others. Had I done this for the midterms, I could have scored high 80s. 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. Professor Starner was fairly involved in the class and answering students questions which made the class more lovely and desirable. It is like you are taught how to cut woods but you are required to build a palace. It is dense and really really hard to follow. And, dare I say they were sort of fun? My local tests were failing but somehow got 100 in GS. The first assignment was easily the hardest in the class- it involved designing an AI to defeat a 2 queen board game. The assignments were presented well, and the requirements were clear, but the testing strategy was poor - the local tests did not evaluate the assignment appropriately, and submissions were limited to actually test it. But if you take this course, be prepared to rely on no one except yourself. End: All assignments can be completed with runtimes less than 30 seconds. Georgia Institute of TechnologyNorth Avenue, Atlanta, GA 30332Phone: 404-894-2000, Application Deadlines, Process and Requirements. The first level, 0 (max), is our turn, so we want to maximize the next situation. So for the subject matters, this course gets a 5 from me. The course material is really good. I found it much more challenging than the midterm and I believe this was due to the lack of relevance to the projects / lectures. Both exams are take-home exams that are 35-45 pages each with a full week to complete. 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. This was my third class in the OMSCS program, my first summer course, and I took it alone while working full time. People got frustrated by unclear expressions in the assignments and exams. This course would definitely help me with both of them as well, on the other hand. AI covers a lot of interesting topics. This is the only class that Ill bother leaving a review because its impressed me both positively and negatively. Overall this was a great class with a lot of fun topics. Most of the coding assignment is not really CS coding I would say. Midterms and finals were both take-home but expect to spend up to 20 hours on each one to do a good job. Std I averaged 20 hr/project and got to 100 on 5 of the 6 projects. Quite frankly for people like me who have ML background, this is probably an easy one. 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. It also contained some materials, barely touched in lectures or readings, while googling was forbidden. The lectures arent quite Joyner quality, but they are reasonably good, although some of the older lectures from Dr. Thrun and Dr. Norvig are a bit potatoey. This is my second class (first was Computer Networks). Each project has multiple sections with tasks of scaling difficulty Each project starts with easy tasks, then scales to moderate tasks, then ends with experimental/difficult tasks. The algorithm is guaranteed to converge, but there also exists local optima, so restarting with changed initial locations may be necessary to find the optimal clustering. then, it is the other player's turn, so we assume they try to minimize our value. Exams are open book with about a week to work on them. Some assignments even had auto-graders which I appreciated because you could roughly know your grade on the assignment before the submission deadline. I think you should be fine. My background is a CS major working as a software engineer at a FAANG. I learnt most about HMMs , Random Forest , Search algorithm only because of the assignments. The hardest thing for me was turning a mathematical expression into code (specifically value iteration and the viterbi algorithm). The remaining assignments werent too bad (I was familiar with DT from ML4T so that helped with assignment 4, and the probability from assignment 3 wasnt too bad either- definitely easier than the Bayes net question on the final.) Take intro to AI from Thrun before the course. The assignments and exams are that good. this is the most struggling and frustrating course i have ever taken. This was one of the best-run courses I have taken so far (I am now halfway done with the program). The midterm gets in between a project submission and it could be a bit more tedious to allocate time during that period. As someone who did not study CS in undergrad, I felt like I started pretty far behind as things like recursive algos and Search were difficult for me to grasp quickly. The worst part? time step (frame) representing right hand & left hand Y positions. Viterbi_animated_demo.gif That being said, the first two assignments were the most coding intensive and most students rank them as the most difficult. Unfortunately, with everything thrown in, some stuff will suffer (and is unfortunate, most of the stuff is worth studying more carefully). I definitely would not take this class as my first AI class. This is one of the best courses I ever had. It was impossible (IMO) to approach any assignment with the lectures only, which presented a problem in itself - if one puts 3-4h/week in lectures, just to have to read another 3h from the AI book, that makes the lectures kind of useless. I felt I was over my head in some of the Bayesian and Gaussian stuff. Instructors/TAs: Each topic comes with homework. I had some background in quantitative field and going through the materials greatly strenghtened my understandings of a lot of ideas. Total 6 assignments. py files they provide werent perfect - lets put it this way. Uses Python for coding. Tips based on this semesters experience: Tough, but fair. When provided with an evidence vector (list of observed Game playing (10 hours) - Supposedly this is the hardest assignment, but we may have lucked out with an easy variant this semester. Learn Numpy and it will be used heavily in later part of assignment. If you cant, thats ok too and next item will help. This course is not a gentle introduction to anything. Just save yourself the money. For this course, we use Bayes Theorem for inference on Bayes Nets and distribution sampling. Overall, its a good course if you have any interest in AI. he led the data science teams at Lazada (acquired by Alibaba) and uCare.ai. pip install -r requirements.txt Overview The assignments were very front loaded with the first two assignments being the most interesting and time consuming while the later assignments took less time but were not as interesting. not sure how in person lectures were, but online lecture werent as helpful as i thought they were going to be. In terms of difficulty, the assignment related to search, assignment 1 or 2 depending on the semester, is the hardest, but definitely doable. All the projects came with unit test which many times did a horrible job of actually testing your code. Both Midterm and Final are a 30-50 pages PDF with open questions/exercises to do at home in a week. This class is very difficult and will punish you heavily for taking too long to start on the projects. I actually enjoyed A1 but A2 was a nightmare. Best class Ive taken so far (out of 4). I believe a big portion of students will get A given the grading criteria (>90 will get A). The course is pretty close to the real deal on AI education. The instructor barely conducted office hours and TA support was pretty basic. Projects are auto graded through Gradescope and generally have 2 weeks to finish them. Big high fives to the TAs for getting grading done quickly. For context, this was my third OMSCS course (after KBAI and HCI), and I got my undergrad in CS. 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. This is a great class that covers a lot of AI related algorithms. Take your time to do midterm and final, review once again to avoid silly mistakes. Not that challenging if you just want a passing grade because its easy to get most partial points. Overall, Ill recommend the class highly if you wish to explore & know more of what AI, ML etc is all about. The exams did a good job of convincing me that I have no idea what the hell Im doing. I have taken KBAI prior to this class and it really doesnt prepare you at all for it. The student had to research and learn on their own how to solve problems and use certain equations. Let's address some problems of k-means: what if some of the clusters are overlapping? I think they only check the piazza once a week. The TAs answer super super slow. I was a big fan of this class. I am struggling to write this review. Are you prepared to spend at least 9 hours a week on this class? He hosted office hours before each exam and that was it. Its unclear if they have responded to the criticism this semester, or Ploetz and his TAs are just better suited, but maybe try to take the course with him if you can plan it that way and that stuff matters to you. First course in anything AI. So much content is covered, it felt a bit rushed. If A and B were still independent after event C occurring, then this logic would hold! So, our head instructor was one of Dr. Starners Ph. They were not that strict at policing it, at least not that I could tell. Its super gratifying, though, if you have the time. 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. It is frustrating at times to implement everything from scratch but satisfying when you finally understood the concepts by working through them. RIP. It isnt impossible, but it isnt easy. They kept a Clarifications piazza post open the whole week, and we never got any question revisions throughout the week, and most of the clarifications they made were very helpful. 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. If you are looking for an A, assuming you have not had any AI courses in the past and/or relevant experience, you should get higher than median for 5 out of 6 assignments and both exams (Not easy). leave the boundary as I will say the exam is not extremely difficult, but it is indeed time-consuming. Looking for nuggets of information only offered in lectures? The projects were also interesting if youve never taken an AI course before. Start everything early and ask questions. Will have PTSD about this course and that final for many years to come. I found all of these policies unusually generous compared to other OMSCS classes. TA interactions are great. Its worth it. Many times you were being forced to write a complicated algorithm from scratch even though the algorithm existed elsewhere. Fortunately there is skeleton code which makes it a fill in the blanks deal, but they are some very big blanks. It shook my confidence. 3) ahead of course. For the most up-to-date information, consult the official course documentation. last year Overall, I think the concepts of this course are interesting and definitely important if you want to pursue AI.

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