• Display Mode: Set to fullscreen to maximize frames per second (FPS) and ensure immersive gameplay.
• Material Quality: Lower this setting to reduce the complexity of shadows and materials, optimizing performance without compromising competitive gameplay.
• Texture Quality: Although this setting minimizes performance, setting it to medium or high can enhance visual fidelity on high-tier systems.
• Detail Quality: Lowering this setting reduces visual clutter, especially in foliage and textured environments, providing a slight competitive advantage without sacrificing performance.
• UI Quality: Keep this setting medium, as it doesn’t significantly impact performance or visuals.
• V-Sync: Turn off V-Sync to prevent input lag and maintain smooth gameplay, especially in fast-paced scenarios.
• Anti-Aliasing: Set to MSAA x2 to sharpen edges and improve visual clarity without heavily taxing system resources.
• Anisotropic Filtering: Experiment with 2x or 4x settings to enhance sharpness on oblique surfaces based on personal preference.
• Improve Clarity: Keep this setting off to avoid potential performance impacts while providing minimal visual gain.
• Experimental Sharpening: Although still in beta, enabling this setting may sharpen texture edges for increased visibility, enhancing the gaming experience.
• Distortion: Turn distortion off to maintain visual clarity and prevent any reduction in gameplay quality.
• Cast Shadows: Enable cast shadows to ensure enemy shadows are visible upon contact, facilitating faster reaction times and enhancing gameplay precision.

• Mouse Sensitivity: For beginners, starting with a higher mouse sensitivity may feel more comfortable, as it mainly requires wrist movements to navigate the battlefield. However, transitioning to a lower mouse sensitivity becomes essential when progressing for pinpoint accuracy. Lower sensitivity settings afford greater control and consistency so players can land precise shots during battle.
• Scoped Sensitivity: For scoped sensitivity, consistency is critical. Setting the scoped sensitivity multiplier to ‘1’ means sensitivity remains consistent between scoped and ADS (aim down sight) modes, promoting muscle memory and precision. Some players opt for a lower scoped sensitivity to increase precision with scoped weapons, but most adhere to the ‘1’ setting.
• DPI: Many VALORANT pros use DPI settings no higher than 1600, with 800 DPI being the most common choice, followed by 400 DPI. These settings balance precision and maneuverability, allowing players to navigate the battlefield with finesse and pinpoint accuracy.
• eDPI: Choosing the proper sensitivity setting is vital in a game where every bullet counts and time to kill is minimal. The ideal sensitivity setting balances precision aiming and swift reaction times to deal with unexpected threats and flankers effectively. The average eDPI settings among VALORANT pros hovers around 267.

1. CSE 6040 - I mentioned how my programming background was weaker relative to my mathematical background. However, reflecting on my experience in this class, it was probably still pretty strong. This class is focused on generalized programming skills, you don't really get into the analytics and modeling that much; it's more of a primer on things like functions, recursion, computer memory management, etc. It's been a few years, but I don't remember struggling with this class at all, and most of the concepts covered (object oriented programming, things like byte encoding, hexadecimal forms, recursion, etc.) were things I was already familiar with in another programming language (this class was in Python) or was relatively easy to pick up. From what I remember, the assignments were auto-graded and you had unlimited attempts, not to mention the fact that most of the prompts were to produce some predetermined result... and, as long as you were able to verify that your code produced that result, you got full points. I didn't miss a single point in this entire class. I believe there were "final exams" which were really just timed window coding assignments much like the homework, and you could do the assignments at any point during a 4-day window or something like that.
2. ISYE 6501 - This class was R-based and focused on basic analytics models. The material was much more applied than 6040. Similarly, the material was not difficult, and I was familiar with a lot of the basic models already (such as GLMs) from having worked with them in my job. The grading was done on a peer-grading framework; based on who anonymously is assigned your homework to grade, you can get someone who's a stickler for every point on the guide, or people who are a bit more lenient. I never really worried much about how things were graded in this class; yeah, I did miss some points arbitrarily, but nothing that made that much of a difference. If you generally put in the work and understand the material, your peers will recognize that. I found this a bit more interesting than 6040 because, rather than a deterministic "right answer", there was some more creativity implied here to solve each problem. There was a final project for this class where you walked through a hypothetical analytics problem and explained how you would go about solving it. I found this an interesting thought exercise and enjoyed this class. The pacing felt a little fast, as you basically had an assignment due each week, but the assignments were small. Like 6040 I found this class to be quite easy but I didn't 100% it due to the peer grading thing.
3. MGT 8803 - I almost applied to be exempt from this class, as my background intersects a lot with the material. The accounting and finance modules for this class literally did not teach me anything new. Supply chain was new for me and I found it interesting. I'm trying to remember what the other module was. I think it was marketing? I didn't like it. Overall I found this class quite easy for the aforementioned reasons, however I've heard from a lot of the other folks without business backgrounds that this class was pretty tough so take my opinions with a grain of salt. Taking this in the summer cut out one of the modules, normally there are 5 but in Summer there are 4. I figured this made sense to take during the truncated semester because I was unlikely to cover that much new material, and it turns out I was right about that. Each module lasted 2 weeks (I think in a normal semester it lasts 3) and has a timed multiple-choice test at the end. A lot of people didn't like this format compared to ISYE 6501 and CSE 6040; I'm not really sure how else this material could've been covered. This class was OK, the material is not really that deep but it's a pretty good primer on a large number of business topics.
4. ISYE 6740 - The first class I took that was actually pretty challenging, which makes sense considering the first 3 courses were just the basic core. This is pretty much an intro to machine learning as a discipline, and the first time I remember digging into academic papers that discuss some machine learning topic and attempting to recreate the results (this is something we did a lot in the more advanced classes going forward and incidentally now that I have graduated is probably one of the best ways to go about learning a new topic). I remember this class as having a format similar to ISYE 6501/CSE6040 in that you had large programming assignments to do, as well as open-book "exams" which were really just timed programming assignments. Assignments are not auto-graded; TA's review each one and thus the assignments have much more of a focus on explaining your findings than producing the exact expected output (unlike 6040). Some of the theory questions have you applying complicated matrix algebra rules that I'm not surprised a lot of people struggled with. The TA responsiveness in this class was pretty good from what I remember, but your mileage may vary. I remember getting 3 weeks to do each assignment but I also remember not thinking that was a lot of time, these assignments are very extensive, have many parts, and take a long time to get through. Like with 6040 I ended up not missing a single point in this class but I did find it difficult and spent significantly more time working on it than in prior classes, probably 10-15 hrs a week, give or take.
5. ISYE 6644 - I was familiar with maybe 50% of the material we covered due to my extensive statistics background, however I was not aware of the exact mechanics of random number generation or the concept of a batched mean, for example. I remember this class having several "check your understanding" quizzes that focused on the mathematical foundations. Didn't struggle with this much in terms of difficulty and found the material very useful. Setting up custom simulation environments is very useful and arena is pretty cool even if it's unlikely you'll ever use it. Some assignments feature similar tools in Python (simpy). There might have been some coverage of R in this as well, or at least the accommodation for people that wanted to use it. There was a project for this class, but you didn't have to come up with the topic on your own, you could pick from a list, and you could do your project on your own if you wanted (which, given the option, was always the choice I made, due to the inherent randomness in picking the right members of a group). However you can do a group project if you want.
6. MGT 6203 - The first of the classes I took that required a group project. I recommend you are proactive in putting together groups in situations like these, posting threads on Ed/whatever the forum is as soon as the class begins. My group was alright; not everyone in it was great, but we had enough going overall to make up for the weaker group members. The project has some arbitrary guidelines from what I remember - you need to put together a midterm report and video presentation that is no more than ~2 minutes long or something like that (if it's 2:01 you get penalized) and the final report is 5 minutes or less, and everyone in the group needs to speak. I honestly don't remember much else about this class, it was pretty forgettable, but not horrible. Not overall that difficult, another business class so a lot of topics I was already familiar with, but there was more new here than in 8803.
7. CSE 6242 - Another class with a group project. Again, I was proactive, and again, overall, my group was... okay. Some people who were really good, some who were... not. This class is characterized by a lot of assignments that are autograded, like 6040, but the assignments are a bit more difficult. Overall not that difficult with the exception of the D3 assignment, but that's more due to the fact that I'm not really sure how the autograder works for that; it tries to determine based on some internal structure of your html code whether or not you're fulfilling the requirements. I got a perfect score on all of the assignments, and they give you the chance to score over 100% on I believe either assignment 1 or assignment 2. A lot of people bombed the D3 assignment (I think it's assignment 2) but still did well in the class because it's not that hard to do well on everything else, so keep that in mind. This class does a great job of exposing you to a lot of new technologies, but there isn't that much depth to it. That's not really the point of this kind of class though, it equips you with the tools to explore things deeper if you so choose.
8. ISYE 8803 - I was a big fan of this class. It's taught in MATLAB but you can use Python if you so choose, you'll see in reviews of this class that you should really just use MATLAB since a lot of the sample code etc. is not in other languages, so that's what I did. However, they must've recently added Python and R code for sample solutions, so feel free to use what you want. MATLAB was interesting, there were parts of one assignment I also used R for (grouped lasso in R is a lot more straightforward). This class is all about high dimensional data and representing it in a more simplified and comprehensive way, think about something like sonar which might have datapoints separated by milliseconds and thus a very dense representation of a signal captured over a short period of time. After ISYE 6740, I found this to be the class that taught me the most up to this point.
9. CS 7642 - Taking this class in summer is kind of rough. There's 6 homework assignments that are autograded, similar format to CSE 6040. There are 3 projects which are much larger programming assignments for which you'll write papers explaining methodology, results, etc. These projects take a while, particularly project 3. I did well on projects 1 and 2 and decently on 3, although I spent the most time on 3 by far; it involves reinforcement learning to simulate a soccer environment and train agents how to play against an AI developed externally. The AI baselines are hard to beat, and I didn't manage to beat them, but I wrote a decent paper explaining what I did. The final exam for this class should be dropped as it doesn't add value to the class, people regularly score extremely low on it, the average score in the class was something like a 45%. I scored a bit lower than average but still got an A in the class because it was heavily curved. Reinforcement learning is a very interesting topic, though, and I would highly recommend this class as a primer on the material. It's probably a good idea not to take it in the summer, though.
10. CS 7643 - This class was pretty difficult but I still think 6740 was tougher. The material is extremely dense. There are parts of programming assignments that are autograded, but also short answer portions that are reviewed by TAs. Grading on those were pretty subjective. This is the only class I can remember really needing to discuss things with TAs to understand what was being asked a little better. Unfortunately, the TAs in the semester I took this weren't the best. They seemed more concerned with unintentionally giving away a bit too much information in any of their responses. I can understand this, but it came off as intentionally opaque most of the time. There was a group project for this course as well, and my group was excellent, probably the best experience I had with a group in this program. I can imagine how much this course would've sucked if I would've had a mediocre/bad group. Based on discussions with my group, some of the grading seemed highly arbitrary, with some TAs grading similar responses to the same question differently. Like I said above, though, I never really worried about this. I never once in this program ever disputed a grade, and I continued with that in this class as well.
11. CSE 6748 - Practicum and final class. For this class you get to choose between a number of pre-determined Georgia Tech sponsors, or form your own project for your own employesome external entity. It was a lot more work to do this, so I just went with one of the pre-determined GTech ones. I really enjoyed this one, I had constant communication with the sponsor as I developed my project and came up with something that I was quite proud of. I wanted to explore a natural language processing task, so I picked a project that I thought would allow me to do this, and was very satisfied with the result. There's a number of videos you have to watch that explain some overarching aspect of analytics that were pretty interesting as well, you can watch all of these in a single day and then focus on the project if you like. It's possible to finish the entire semester's work in just a few weeks, I was able to do the entire project and write the final paper in about a month's time, at which point I coordinated with the sponsor to tailor the work I did to a format that they would be able to implement for their business problem if they wanted to.

1. I'm pretty shocked at how many people I saw during my program who didn't really think that much about why they're doing this. I get that the barrier to entry is low, but it's a serious commitment if you're actually trying to graduate. Most of the people who start this program don't finish, so consider whether you're ready to spend almost 4 years going to school part-time, or if you're able to double up on classes for some of the semesters. Most of the people I know in the program doubled up at least once, I never did but I was never in a hurry. If you must double up, don't make it your first semester. Dip your toe in the water, see how it is, and then reassess. But, above all else, think about why you want to do this, and use that as your guiding goal to bring you through to the end.
2. Something I tended to see pretty much without fail in most of my classes - a lot of the graduate students in this program spend way too much time worrying about minute, particular details that don't really matter. Maybe it was just my philosophy that I would probably never dispute a grade, or that I was never really that concerned with getting a perfect GPA, etc. but I was always marveling at what I saw asked in Ed posts. People would ask whether they could use a certain programming language for an assignment, what packages they were allowed to use, would post screenshots of bugs and ask for TA's to help walk them through it, etc. Generally, without fail, the TAs would respond along the lines of: use whatever programming language you want, as long as you can display your output/submit it in a way that we can verify by running ourselves, we'll make the effort; use the debugger to step through your code to find the problem; etc. Generally, in most cases, the assignments and questions are designed in a way to teach you something, to get you to realize/understand some pattern or data concept that has some underlying logic that makes sense. For example, the idea of saliency maps on image processing takes the 3-channel RGB color pixel shading representation of an image and condenses it into a single channel, and, as a result of that, loses some resolution in suggesting parts of the image driving a model result that might be different depending on the channel; i.e., an image with a very heavily blue-shaded part that detracts from a certain result, but with a red-shaded part somewhere else that increases the probability of the modeled result. This was part of a conceptual question on how saliency maps differed from other pixel influence attribution methods in Deep Learning, and is part of what you should logically understand since it reduces the channels of the image representation from 3 (R, G, B) to 1 (usually grayscale). I think people tend to run to the TA the second they have difficulty with something and don't stop for a second to think it through, one exercise I might recommend is to consider: if you ran into this problem out in the world and you didn't have a TA/managesome other authority figure to explain the answer to you, what do you think it might be? Does the answer even matter? If it still matters and you have no idea how to solve it, maybe then you can go to the TA.
3. In every single group project I worked on, we had an initial planning session where we determined the scope of what we wanted to do. For most of the projects, this was an essential deliverable in addition to the final paper. However, in almost every case, someone in the group was always playing some game of runaway scope where they kept on wanting to add methods/questions to exploration beyond what was initially planned in ways that I intuitively knew would be impossible to manage in just one semester. I often had to say something along the lines of "if we have time we'll do that" or "when we write up our paper, we can put that in the avenues for future exploration section" or something similar. It turns out that we never had time to look into these things, and our initial scope was usually well-defined considering the time we had. I'm not sure why this was always so front-and-center in my focus, maybe since I used to work in consulting and project budgeting/scoping is so unbelievably important in that context. Whatever the case may be, understand that you won't be able to change the world every time you do a project. Make some incremental improvement, reflect on the results, and then include some notes in a "potential avenues for future exploration" section. I was pretty surprised at how many people had so much trouble putting the pencils down at the end. I can practically guarantee that, for the classes where I did a project on my own, I probably did substantially less work than other individual groups for precisely this reason. In general, you probably don't have to do as much work as you think you do.

• Good servers (to me): Emphasis on not being a dickhead, actually gets players, if running custom content it has to be tastefully done, not bloated & tacky, ping <100, anti-teamstacking measures
• "Bad" servers/what will not be here ( again this is just my opinion, also a couple exceptions ): Instant Respawn, 1 map or game mode only, more than 32 players, RTD, "friendly"/no combat servers, unmoderated racist/toxic BS
• Servers/settings I'm indifferent towards: Crits/Nocrits, Vanilla vs. Custom, Average skill level, 24 vs. 32 player, NSFW sprays, centered around niche/"weird" fandoms, Bullet/Damagespread, Class limits

Scroll down a few lines to my "To keep it simple..." header for the best ones. The following link is a much fuller list:

• Green line ones are my seal of approval
• Pink line ones I either haven't checked out as much but seem to be ok, or have 1 big issue or 2 I think holds them back from being fully recommended
• No underline = probably seemed interesting, haven't checked out at all, or never gets full

To keep it simple, I think the best ones right now (for me are):

• Skillcheck Chance
  • Default: 8% per second
  • Deadline: +10% per second
  • Hyperfocus: +4% per stack
• Bonus Progression
  • Default: +1% charge
  • Hyperfocus: +0.30% per token
  • Stakeout: +1% charge with minimum 1 token
  • Bardic Inspiration: d20 [2-10 = +1%, 11-19 = +2%, 20 = +3%]

• God-Level: NO Stakeout
• Intermediate: NO Hyperfocus
• Two Perks: USE Deadline + Bardic

import "random" for Random var Roll = Random.new("Tryndamere".codePoints) var Results = List.new() var Repair = Fn.new() { var time = 0 var generator = 90 var charges = 0 var bonus = 5 // Default +1% Progression Bonus + Bardic Inspiration(r20[+3%]) + Stakeout(t1[+1%]) var focus = 0 var debounce = 0 // Game seems to reduce the number of skill checks that appears while (charges <= generator) { time = time + 1 charges = charges + 1 if (debounce >= 2) { debounce = debounce + 1 continue } var roll = Roll.float() if (roll <= (0.18 + (focus * 0.04))) { focus = (focus + 1).min(6) charges = charges + bonus + (focus * 0.3) } } return (time - 1) } for ( i in 1..10000) { Results.add(Repair.call()) } System.print((Results.reduce {acc,ele acc + ele })/Results.count) 

• New (Started 05/14)
• Versions: 1.20.4 to 1.20.6

• New (Started 05/14)
• Versions: 1.20.4 to 1.20.6

• New (Started 05/14)
• Versions: 1.20.4 to 1.20.6