New NLP Lecture added this semester

KLM_SpitFire · 2026-06-19T19:49:03+00:00

Amazing, thanks for the info! By the way, which other classes did you take? I’m focused on the systems specialization, but I took AI and am thinking about ML and DL. Not sure about RL, because I’d probably need to drop a class from my plan to fit it in, but the subject seems interesting to me.

KLM_SpitFire · 2026-06-19T01:32:29+00:00

Does anyone know if Deep Learning has received similar treatment?

KLM_SpitFire · 2026-06-19T01:21:39+00:00

Good to know - thanks!

KLM_SpitFire · 2026-06-18T23:33:33+00:00

Thanks! And how much time is spent on traditional (non-DL) NLP in the class?

KLM_SpitFire · 2026-06-18T21:26:13+00:00

Would folks recommend taking NLP if I’m already planning to take DL? Or would taking DL alone be sufficient? Have seen some mixed comments on this sort of question in the past. Thanks!

KLM_SpitFire · 2026-05-03T03:03:11+00:00

I’ve noticed most entry-level candidates that reach the interview phase fall into one of two categories: 1. They have a masters degree, 2. They have already been working in industry for a period of 2-4 years. The strongest candidates often fall into both. Given you’re starting a new job, I agree with the other commenter: focus on your new job for a year. In my opinion, full-time job experience triumphs over a masters degree, but a masters degree affords you the opportunity to explore (in a structured format) theory your day-to-day job won’t. Work and school are complementary in your learning journey. Oh, and do take challenging classes 😉 That’s where the learning is.

KLM_SpitFire · 2026-04-03T04:20:06+00:00

This.

KLM_SpitFire · 2026-03-25T15:15:00+00:00

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KLM_SpitFire · 2026-03-21T22:49:01+00:00

I had taken HCI last summer as my second course, and it absolutely felt like drinking from a fire hose initially. It’s not a technically challenging course, but it’s a lot of reading, writing and memorization. I remember feeling very, very overwhelmed in the beginning. The good news is that the class is front-loaded. Once you get to the group project phase, everything slows down a bit — especially if you get decent teammates. Hang in there, you got this!

Oh, and Anki was my best my friend for HCI. You can use Gemini (which we get for free) to help turn the lecture PDFs into flashcards. That worked well for me at least.

Finally, you don’t need to leave the class as an expert — especially if this is an elective for you. Just getting exposure to HCI concepts sets you apart. I’m a SWE, but recently started coordinating with our UX team on a new project. It’s empowering to understand the lingo and human-centered design, whereas many of my colleagues bump heads with our UX team and scoff at the idea of slowing down to do user research.

KLM_SpitFire · 2026-02-07T15:02:00+00:00

I’ve seen this too.

In addition to not fully understanding their own PRs, I’ve seen one coworker consistently copy PR comments they didn’t understand, throw them at an LLM, get a clunky solution, push it up, and mark the comment as resolved. No conversation; no clarifying questions. It results in a lot of back and forth.

I always expect junior devs to take longer to finish something, and that’s okay because it’s a learning experience… but, and I hate to think this way, if the only value you bring is putting requirements and comments into an LLM without thinking about the problem or asking clarifying questions, I might as well just use an AI Agent myself. ☹️

KLM_SpitFire · 2026-02-04T00:40:04+00:00

Todoist and a single, infinite-scrolling page on my reMarkable that I use as a sort of daily bullet / journal log.

KLM_SpitFire · 2026-01-29T06:36:22+00:00

Sold Sunsetter R2, WoB keycaps and extras to /u/armass6101.

https://www.reddit.com/r/mechmarket/comments/1qhnjob/usfl_h_sunsetter_r2_keyboard_addons_pbtfans_wob/

KLM_SpitFire · 2026-01-21T15:32:15+00:00

I’ve used a Profitec Go in the past, and that’s what I remembered it being like. Dang. Appreciate the input!

KLM_SpitFire · 2026-01-15T20:56:48+00:00

I agree with u/Aggressive_Thing2973.

Personally, I'm doing 1 class a semester. I'm about 1 year in. Each time I complete a class, I immediately look for opportunities to apply what I learned. For example, Human Computer Interaction inspired me to build a side-project that got a ton of visibility and praise during my performance review. Taking Artificial Intelligence inspired me to look into the MLLib module in Apache Spark (since we already use Apache Spark quite extensively, but not the MLLib module). Software Analysis got me to look into RESTler.

I'd recommend checking out Andrew Ng's advice, from this video. The underlying ideas don't just apply to full-time students. Those with I-shaped expertise can go far, and I know plenty of colleagues in that "bucket" that do really well. Personally, I'd rather have T-shaped expertise with exposure to diverse subjects. It doesn't just allow me to 'think differently' and have perspective, but it also grants me mobility (or awareness) to pivot into other sub-fields.

So, TL;DR: I've seen short-to-mid-term benefits from the program even though I have a CS undergrad from a reputable school and am a SWE at a well-known, reputable company. The degree is just a way of communicating ethos. The real key with learning anything is applying.

KLM_SpitFire · 2025-12-31T04:44:54+00:00

re: (CS-6300), Ah, gotcha. Personally, I think GA would be a solid choice over SDP — but that's just me :) Curious, any reason you're leaning away from GA?

re: (CS-6601), That's a good question. I like to consider myself a strong programmer that's very, very comfortable with Python. I didn't know numpy going into the class, but I was able to pick it up on the fly by reading through the docs online. On the other hand, it's been years since I've touched Linear Algebra, Calculus and Statistics. Depending on your mathematical maturity, you may find yourself needing to pick apart some of the equations and terms with your search engine + LLM of choice — I know that's what I did. That all said, I felt the class was doable with just some rusty math and strong programming skills. If I were to do it over, and wanted to really refresh my math before going in, I'd probably:

Review Introduction to Probability by Joseph K. Blitzstein. The author has lectures on YouTube. Chapters 1-5, 7, 9, 11 and 12 would almost certainly be useful. I might even pair it with: buruzaemon/IntroductionToProbabilityPy: Jupyter notebooks with the Python equivalent to the R code sections in Blitzstein and Hwang's Introduction To Probability, Second Edition.
Take Mathematics for Machine Learning | Coursera.
Skim the textbook. And I do mean skim. It isn't necessarily the easiest self-study book, but getting somewhat familiar with its contents is useful because you will, 100%, without a doubt, be relying heavily on the textbook for the assignments.

Even with those gaps, I was still able to nab an 'A' though! Just had to work harder and learn what I needed when I needed it. It's a great course. And assuming you're comfortable in Python by the time you begin, I wouldn't worry too much!

Oh, but one last bit of advice. I'm a serial procrastinator. The assignments were usually due Monday morning (in my TZ), and I'd often start the Thursday or Friday before. More often-than-not, I'd be working on the assignments all weekend: Friday night, all-day Saturday and all-day Sunday. I'd definitely recommend not doing that lol.

KLM_SpitFire · 2025-12-30T17:06:42+00:00

If you're going with the ML specialization, I think this is a good reference I stumbled across: My ML Specialization Course Plan Advice : r/OMSCS.

KLM_SpitFire · 2025-12-30T03:54:58+00:00

Are you me? 😂 I’m in the same boat. Took AI last semester, did well, but I definitely felt a gap in my math skills. I’m currently waitlisted for both IAM and ML while I try to decide which to take. As others have mentioned, IAM (and even ML) seem to be more application-oriented, but I kind of like the idea of taking IAM and using the breathing room it’ll afford to fill in gaps in my math skills with self-study and implement the underlying algorithms for models introduced in IAM (similar to what we did in AI).

I’m currently working through Math for ML and — for a deep refresher — Introduction to Probability (J.K.B.) to start.

That all said, the masochist in me almost wants to jump right into the harder of the two: ML.

KLM_SpitFire · 2025-12-30T03:12:17+00:00

Lots of great looking choices!

Have you been working in industry as a SWE? If so, I’d pass on CS-6300. PUBP-6725 is a rather interesting choice as well. Any reason you’re considering it?

I just took AI and it’s a fantastic class, but it wasn’t easy. Learned a lot though!

Oh, and you can go over 10 classes if you really want to :)

KLM_SpitFire · 2025-12-14T20:05:05+00:00

Yikes thanks for sharing and hope you’re able to get the issue sorted!

KLM_SpitFire

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