u/abrbbb

I'm a software engineer with a mediocre understanding of linear algebra, probability, and calculus. I'm taking CS 7641 (Machine Learning) and struggling with the math, proofs, and derivations, although I understand the concepts well enough. I am doing fine on the projects (SL and OL at least) via LLM help with the code, and well enough on the quizzes via the retake mechanism.

Can I still get a good grade in this course without fully understanding the underlying math? I don't plan to pursue ML beyond this course, my goal is to have passing familiarity with ML and enough to get more into it if needed at work, so I'm specifically interested in what it takes to do well in CS 7641 and not how much math I'd need in practice.

I haven't slept in a week

My employer may be willing to cover the cost of the course depending on whether it covers certain topics relevant to my work. Does anyone have a syllabus, course schedule, or detailed list of the topics covered? I'd especially appreciate details about how much depth the course goes into for each topic. Thanks!

I was reading a Karen Kalsky post about academic interviewing, and she writes:

>If you encounter a Dean who is taking the intellectual tack, you need to be prepared to speak broadly about the most important current debates in your field. An “intellectual Dean question” will sound something like the following:
“What do you think are the most important current debates in your field?”
“How do you think your field will change the most in the next ten years and why?”
“What is the single biggest challenge facing your field right now?”
“What is the most important text published in the last five years in your field, and why?”
Sadly, most job candidates are ill-prepared indeed to deal with the intellectual Dean’s line of questioning.

I’m a social sciences PhD student who cannot answer these questions in my field. How can I develop a better understanding of them? I'm based in the Middle East, which means I have less access to the latest developments and cutting-edge conversations happening in the US, but I'd like to become more involved in the broader US academic landscape after completing my doctorate.

My PhD advisor's intermittent emotional rewards are driving me crazy. Sometimes she's friendly, sometimes she's not, sometimes she answers emails right away and sometimes she waits two to four weeks. It makes me crazy trying to predict her or understand what she's up to. I deeply admire her, and deep down she is a nice person, but I hate that I keep refreshing my email to see if she's responded, especially when she always answers her other PhD student immediately.

What can I do to emotionally stop being addicted to the cycle, and let go, and stop wanting her approval?

(I am hitting all milestones and more, it's not a performance issue.)

Feels like all the major old guard figures (eg Marty/Appleby, Sivan, Peter Berger, Bruce Lawrence, Stark and Finke and Bainbridge, etc) are no longer active.

As an early stage researcher I'm fascinated by memoirs, autobiographies, and extended interviews with major figures in the social sciences and humanities.

I've found these especially interesting so far:

- Interview with Theda Skocpol

- Documentary on James C. Scott

I'm particularly interested to material where scholars talk about how they worked, academic life, intellectual evolution, institutional constraints, personal life problems, etc. not just what they concluded.

Title says it all. As there is waitlist room only how do I register? This is my final course for graduation.

I'm unexpectedly stuck in Nicosia for a few days and am kinda hungry and looking for ideas.

As a new PhD student in the social sciences I'm curious to know.

Am I missing something? I put the CRN "85134" into registration and get an error "major restriction prerequisite and test score error".

Trying to figure her out. Context is social sciences

Lots of questions here about STEM and working in labs, but I'm curious what your most useful/enjoyable RAs bring to the table.

I'm taking Graduate Algorithms and am currently working through HW2 (Divide & Conquer) after finishing HW1 (Dynamic Programming). What's frustrating is that in both cases I generally understand the algorithms once they're taught, but I struggle to recognize when a problem is actually asking for one of them. For example, with a problem like Koko Eating Bananas, I know how binary search works, but I can stare at the problem for a long time and never make the leap that it's a binary search problem. Then I look at the solution and immediately think, "Of course, that makes sense."

It's making me nervous about the exams because it feels like the challenge is really recognizing which tool applies to a new problem under time pressure.

For those who've done well in GA, how did you develop that pattern-recognition skill? Are there specific ways you learned to identify the "tells" that point to binary search, DP, divide-and-conquer, etc?

r/hypotheticalphysics gets a post every few days from someone with only high school algebra claiming they’ve discovered a new grand theory of everything, validated by LLMs. What’s the equivalent in your field? Not just obvious crackpots, also confident, sweeping theories that collapse under the weight of scholarship anyone who’d taken Your Field 101 would know about.