At last, dont waste your time attending office hours. As a result only got ~70%. To say the least, this class is a mixed bag. However, small mistakes can cost you greatly, though they do provide partial credit (pro-tip: attach all methodology for a chance of getting partial credit). The assignments in this class, ESPECIALLY the first two, are very very very time-consuming. Lectures were mediocre. The flipside here is that if you are taking this as a first course, with no experience in AI, and you want to get the max out of it, youll have a daunting journey ahead of you. Genetic algorithms are a global optimization technique, best known as a method to solve NP-Hard problems like the travelling salesman problem. The overall area is significantly reduced. The course content is organized and prepared well. Since ASL That being said, Id take another class like it in a heart-beat! The assignments and exams are that good. I say it all the time, Ive already hinted at it above, and it will always need to be said. This is not a learn how to code class, you need to come in with strong fundamentals. 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. First one search - if you have a CS background or experience working in the IT industry for a year or more - it shouldnt be an issue for you. Office hours are mostly useless, I did not watch any of them. They also make a myriad of mistakes they have to go back and correct, which is a pain for someone like me that starts as soon as the exam is released. These individual signs can be seen in the sign phrases You know going in that you will be going beyond what youve done so far (looking at the practice exam shows you this will happen), but it was way more than expected. The topics were mostly not relevant to any of the projects or covered as key concepts in the lectures or book. 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. I learnt most about HMMs , Random Forest , Search algorithm only because of the assignments. 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. Fellow students were very helpful on Piazza. This course had 6 assignments in total. To be setup for success, Id say know your python/numpy as well as you can. Constantly asking questions to clarify the ambiguous wording. It may be worthwhile to have extra time in order to triple-check all the answers since theres plenty of rote calculation involved. I found the book to be a necessity. This gives you a nice buffer in case you struggle with something and now you have time. ?), opening the course with adversarial search instead of with actual search algos, and many other small issues. The six projects were all unique and very interesting. I found this to be a much better approach to exams. The midterm and final were week-long take home tests, and they took basically all week. Some assignments even had auto-graders which I appreciated because you could roughly know your grade on the assignment before the submission deadline. In my exam I learned about CNN convolutional neural network, which both explained a final project topic in my other Computer Vision course, and introduced me to another Deep Learning course. This was a tough class, but I enjoyed many aspects of it. The assignment medians are also very high. Then when we got the answers there were more mistakes in them and the exam was re-graded for everyone to account for that. Even with this small issues I have really enjoyed this course. The midterm was 30-something pages. If you dont start assignments early, you will drop this class or ruin your GPA or wont graduate (if youre in the Interactive Intelligence track). . Looking for nuggets of information only offered in lectures? My undergrad is in Mechanical Engineering, really interesting topics so it was easy to stay engaged and not be board of feel like you are working on something that you will never use. 50, 50, 49, 47, 39, 39, 38, 38, 50, 56, 61, 50, 50, 49, 39, 38, 38, 61, 67, 67, 67, First time this was offered as a Summer course, and they did an excellent job adapting it to a shorter semester while still maintaining as much course material as possible. 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. books was good (as much as i could keep up with reading it) but also there were a lot of resources online to help, TAs were great help during office hours and on piazza, love coding in python and this was all in python. Pros: I preferred the lectures taught by the professor (vs the ones taught by the guest lecturers). Overall, I enjoyed the first half much more than the second half. The opinion of others will differ from my own, but make sure you have the time to commit to this class. Additionally, I can assure you that no one who knows me would consider me any where near a genius. 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. Id suggest testing on the reading more and less on outright coding. I was kind of confused by people who started the final as soon as it was released and then complained about clarifications. The first 2 assignments are extremely time consuming, and the midterm and final exams are beasts. Assignments and exam questions often require that you go further than the lectures, and even in some cases, the text can take you. Artificial Intelligence covers relevant and modern approaches to modelling, imaging, and optimization. In the beginning, Thad makes sure to let everyone know how serious they are about cheating. I had taken KBAI the summer before which had given me some good experience in Python and some Numpy. I was basically forced to take it as it was the least worst class available. This course is not for the faint of heart. Try to get a study group for exam prep, we did this for the final and i learnt some stuff i probably would not have otherwise. I would have liked to see more challenging projects where we used these techniques to implement more complex programs instead of writing our own algorithms from scratch. I would rate it somewhere between medium and hard, so I rounded up to hard. Note, I got Bs on the midterm and final median was 78.65% and 67.8%. This is what the TAs told us, verbatim from Piazza: You can use either. If you fall behind on the readings, the exams will take you some time. The vibe of this class was so casual. They are approachable with good preparation. Each assignment takes more than 40 hours. most of the time i made a small mistake that would pass local tests but fail the submission and had no observability. If you follow the same routine, you will end up The good: Best class Ive taken so far (out of 4). The greyed out nodes can be ignored to still reach an optimal minimax strategy. Instead of acknowledging the mistakes and thanking students for pointing them out, they would get defensive and write things like that will also be accepted because we didnt specify how to do X. Welcome gift: A 5-day email course on How to be an Effective Data Scientist . They were generally not responsive (at least in my section of Piazza) or they would only respond to the low hanging fruit questions and leave many other questions unanswered. After assignment 1, unfortunately, everything went downhill. I later realized what I wanted was more under the umbrella of machine learning or reinforcement learning, but alas! . With this level of high caliber students, that is extremely tough. Even the professor remarked that the challenging questions threads had no activity. 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. This is horrible when you have less than two weeks to work on the assignments and you need a clarification. They are both hard and extremely educational. I guess the takeaway from my word vomit is that this class has a lot of inconsistencies. The TAs have also given assignment walk-throughs, of which I attended only one; if you want someone to read the instructions to you this is helpful, otherwise youll be left wondering what the point was. Ive enjoyed the class (aside from the rough start on project 1) and have learned quite a bit. Whenever algorithms are provided, they are pseudo-code. 2) Do not expect to learn much from lectures. There was also an extremely slow response times for questions on the final. For most of the assignments, there is limited number of submissions and provided local tests are not adequate. There is a special move, the swap, where you can swap spaces with the other piece, but this time you can move through the blocked spaces. 35, 35, 43, 46, 52, 52, 56, 49, 45 Oh Im going to take this class so I can learn AI. Nope, shut up. Ive popped into a few office hours and these were a joke, dont bother. Moreover, the TAs were probably understaffed as they were not very responsive. The projects are the core and there are 6 projects, out of which 5 are considered for the final grade. Students only posted on assignment-related threads. Good at recursive algorithms? (limited to course material) so theres nothing to memorize before the exam. The lecture videos quality is a bit disappointing as I found many concepts were not explained well and ended up going to youtube watching some other videos to understand about some concepts. Really, theres more than enough content in this class to fill a semester. There was one where they just linked a YouTube video and told you to follow it. Aside from the Assignment 1 issues mentioned in the Lowlights, these were a good experience by condensing real-world problems into objective, 2-week assignments. Every assignment uses Gradescope for submission and runs a test suite against your code. . Only do readings during the semester. My favorite editor is, Relatively straightforward Midterm & Final Exams, make sure you do your best for assignments (1 out of 6 can be dropped), attempt the bonuses and collect as many points as you can from them. Went up 2 spots on the private leaderboard =). 36, 44 Of the 8 courses Ive taken in the program, this was either my first or second favorite. I liked this course for the content. If one has less programming background, consider preparing by learning Python/Numpy, a bit of search algorithms and probability before starting the course. 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. 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. The material can be math heavy. As for topics, midterm topics were straight from the lectures. Initial They dont do a good job explaining subsequent assignments, and much of my time was wasted trying to figure out the assignment instead of understanding the lectures and reading the book. I spent at least 20 hours on each one. And focuses on depth in the topics of the assignments. Its meant as a proxy to trade secrets in industry, but its nonsensical, especially given the poor resources of the class lectures. they dont actually care, or want to help, and why would they? Be prepared. No final exam. 4: I am glad I took ML4T before this class since the way it explains DT/RF in this course is over-complicated. I was able to complete this one in less than 2 weeks with 92%. There isnt anything. Eugene Yan designs, builds, and operates machine learning systems that serve customers at scale. The program inside, Each node has 3 options. Recommend this class for some historical context on AI and broad survey of the field. As others mentioned the pace of the course is very fast and it covers alot of material (To excel at AI, one requires at least two or three semester to learn the topics that are covered in the text book and the lectures.). Not sure if this was just this semester, but the TAs in the RL class were much better. You dont need to be a python guru to do well in the class, but you should be comfortable programming in it. Hopefully on future iterations the TAs/Staff will figure out how to lock it down so that they dont have to worry about future students finding the previous class forums. This course would be best to take not as a first course, but its high-level enough that I wouldnt push it off until the end either. The exams mostly involved (somewhat tedious) calculation (by hand), through which you learn how the algorithms work and gain practice, as well as demonstrate your understanding and ability to apply and implement. Here is my advice: Prepare for heavy self-learning. I didnt take any time off work as some others mentioned, but it was absolutely among the busiest weeks Ive had in OMSCS. Here you are given the transition probabilities and the emission parameters of left-hand Y-axis locations, following the The Viterbi algorithm is a method for finding the most likely sequence of hidden states. 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. I really liked that. The projects are very diverse so if youve never seen some of these topics until this course, then your learning curve will be longer for each project like mine. Profs office hours were interesting and not just for the sake of getting help with assignments. A GMM consists of different Gaussian components, and the joint distribution is described by the weighted average of the individual components. I only wish I had taken this class before other classes like ML, RL, but I guess then it would have taken me like 1. Hated the exams. Grab recent semester syllabus and go into course schedule. Whatever you do dont try diminish the experience of being lost by asking for clarification. I didnt fully understand every part after watching the videos. All resources available (though not confirmed) before course start is also a huge plus. The clarifications thread was longer that Rapunzels hair. They are take-home exams, you have a week, and you can use materials from the class. Im half joking, but also pretty annoyed. The lectures are a bit dated and could probably be updated and improved. You need good planning skills to go through this fast paced course. This course could have easily been broken into at least 2 parts, one probabilistic (Bayes nets, decision trees, others) and one deterministic (A*, constraint programming, adversarial search etc). observations. Topics are super interesting and important. 0.1 stays 0.1 or 0.100 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. For context, this was my third OMSCS course (after KBAI and HCI), and I got my undergrad in CS. The majority of the comments say that this course is hard/very hard. other fields. The following diagram shows how the There were several first-time hiccups in assignments and exams, however the TAs and the professor had open ears and minds and ultimately made it right and Im sure that the next offering of this course will be even better. It can be true if you do not have a good understanding of foundational topics in algebra and statistics. What is the probability that the squad will have, A text file words.txt is given, which contains several words, one per each line. I felt that these were structured with the intent of getting students to learn the material better while doing the exam, and less of a strict evaluationindeed, I learnt a lot of extra material from doing the exams. They kind of stare at the camera awkwardly the whole time like Godzilla is coming at them. Learn Python; you dont have to be pro at knowing every python syntax; it is not what the course demands. I took three days off work for the final otherwise I wouldnt have been able to complete. Another guest lecturer is Sabastian Thrun, the creator of Udacity and founder of Google X and their self-driving car team. {6} Course is trying to be wide and not deep. Hidden markov models (13 hours) - Relatively straightforward. Come in understanding python and numpy. An interesting application, for which we had to solve a mini-version of, is multiprocessor scheduling. don't have to use gaussian_prob this time, but the return format should be identical to Part 1b. The secret is that it is bad. I thought most of the projects were made intentionally time consuming without much support in the concepts themselves. With that said, I still think this is a worthwhile class to take, I learned so much. With that said, the entire rest of the course besides the exam was well done. Assignments are super interesting and intense I spend almost over 20 hours on each assignment, but they are really helping me understand the materials. So if you assumed you were good because supplied unit tests passed that would be a dangerous assumption, especially as you get toward the assignment submit date. 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. Most of the video lectures were great. Assignment 4 was the easiest for me. This course will give you the best overview of the field. This allows us to assign data to a cluster by some probability. People criticize the lectures in general, but I dont think thats fair. 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. The assignments are also very well done, I sort of wish there was one more on RL at the end because I am a big believer in learning by doing, but I guess there is an entire RL course for that. The book is great for the first half of the semester, and ok for the second half. For some, you could submit an assignment twice within a half hour window up to the deadline (and believe me, I used all the submissions I could and submitted some assignments 30 times). If you cant, thats ok too and next item will help. It was not as hard as before. Generally interesting and well run course. A great difference from ML is that ML focuses more on bench-marking/ comparing different algorithms, but AI is the opposite, asks you to create algorithm from scratch. These projects weed a lot of people out of the class. So, prepare before the semester begins; you will see the course lecture when the semester begins but for early preparation go through: Now when you see the course material, it wont be first time. Ive lived in this room for 3 weeks straight, havent left, trying to complete this assignment, and best I get is a 75. Exams: They are doable, so dont panic. Not sure of this, but only a hunch based on the fact that it was so much more difficult for me. 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. I struggled the most with the third lab and this is where I understood why this class is considered hard. You are given an algorithm, a research paper or two, and told basically have at it. I am sure that youll be able to find much better courses on AI outside that are probably free; in fact, thats what youll end up doing anyways: watching YouTube videos to finish assignments, because none of the provided material helps. In the weeks I was actively working on an assignment, the hours spent in the class went up depending on the assignment. The biggest downside here was pacing. I recommend you to watch the lectures in advance, before taking the class, if you can. Although, I could see the time commitment being difficult with a busy work schedule or another course being worked in parallel. 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. If you plan to take this course, bare in mind that it will require you to keep a rigorous schedule for studying, which must also be flexible enough to postpone other priorities to allow for more study time. There are two exams and six assignments, but you only use your top five assignment scores. Even after passing all of the local unit tests for a given assignment, there were times at which Bonnie tests would fail, and no information was returned about the reason for the failures. The majority of the search algos you asked to implement are part of the pa. Second one (game playing) is pretty straightforward as well, pseudo code is available in the book, so the only thing you need to do is to implement it. Most problems probably due to first time offering. You would spin your wheels for hours and hours trying to find a bug with no idea what was wrong. 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. Mean 56.300 37.110 50.000 The class progressed on a similar tone until the end. The final was similar to the midterm in format but even more challenging and comprehensive. Modified local test case 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. 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. Better yet, do it both ways to check yourself. You will spend most of the time on coding assignments and will not have much time left to go over the material deeply. 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. They host 2 -3 office hours everyday which is super helpful. I do not think that is the case here. The tests and programming assignments are very difficult and will require a lot of time. If you attempt and get through all of the assignments, you will feel amazing about the course. I have found the communication on mediums such as slack and piazza from my classmates to be incredibly helpful to my learning. The textbook is good although heavy on math-y notation. These extra credit assignments are explicitly harder extensions of the already difficult projects. The final 3 assignments had very little to do with the final exam which was surprising to me. Finally, the lectures gave a 30k ft view, but the real learning came from the book, papers, and projects. part_1_a_probs.png Exams are take home, but are extremely hard and time consuming. The feeling of getting a 100 on GradeScope after grinding it out for hours and hours over the course of a week and a half is fantastic. This is my 5th class in OMSCS. As the majority of the people here I consider this course hard. 10/10 would recommend. When you take this class, those other concerns get put on hold. Do all the extra credit. Example: Assume you've reached a stage where the following is true: Youll find yourself learning as much during the exams as you did during the homeworks. This is somewhat solved by an offline testing suite but it is often limited to the most basic things. Id absolutely recommend the class, but not as a first class unless you have a good handle on things. HOUSE State 1 State 2 State 3 I think Dr. Starner said that they had listened to feedback from past students saying the exams were too long, and this semester they cut them down to be more realistic, and I think that they were. Its basically a series of quizzes that assumes you already know it. You got to be careful copying code from the internet from Github, etc. Unless youve got a 100 on five projects, dont think that you can skip one. Gives you an opportunity to review the material well before answering. Assignment 5 was skipped for the summer session. I wish I could go back and take it again. There were numerous clarifications for each exam, even up till the last few days of the exam. There were wikipedia links to start learning mathematical concepts MID EXAM. 72, 75 All the grading is automated, so they really only occasionally clarify things on piazza. So rather than spending time to really understand the new algos and ideas presented, you just end up spinning your wheels to fill the gap where the instructors team was too lazy to make this course really shine. Its the classic joke where the teacher says 1+1=2 in the lecture, and then the assignment is 2+2= calculate the mass of the sun. So I suggest you brush up on your python! This was a great course and one of my favorites in the program. Got the impression that each TA was assigned the task of creating one homework for the class, but then those assignments werent tested or validated by the other TAs/Instructor prior to launch. ( Warm-ups is a misleading term since it will take you more than 50% of the time and around 50% of the grade too), (This review was written half-way through the very first semester , however a lot of people already get exhausted after Assignment 2 ), easy, procedural solution for a full Bayes Network, the problem was a variation of this Stanford problem. The midterm was lengthy but fairly straightforward if you took your time and made sure you understood the question. ). For many questions, if you make mistake in the first 1 or 2 steps, all subsequent steps will be wrong and you will loose all marks. 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. Theres also plenty of extra credit to make up for poor exam grades. (Youll notice other reviewers think this is a bad thing, but I think its actually a positive that we get to learn things during the exam.) The first two I thought were conceptually the easiest, just very tedious to do, Id just leave it if you have like a 90 and got busy.
Bee Gees Islands In The Stream Original, Radiance, Aura World's Biggest Crossword, Lone-r Pianist Moonlight Sonata, Athletic Bilbao U19 Soccerway, Rowing Vs Walking For Weight Loss, Another Word For Military Unit Crossword Clue, Violin Concerto In A Minor Bach Analysis, Red Line Accident Chicago,