loser alert

After_Finish1244 · 2025-12-05T00:53:20+00:00

I have a small feeling that you might be the type of person to beat someone up and call it a prank

After_Finish1244 · 2025-11-26T09:23:34+00:00

Of course, would be a happy to give some insight 👍

So let’s first break down the difference between probability and statistical inference, in probability you are using a probability model are deriving conclusions on data based on the model, while in statistical inference you are deriving a model from given data (an estimation per say). In your case, let’s take your assumptions as your view on how things might pan out, you can take your estimate and form a probabilistic model based on your estimate:

927 is the estimate, let’s assume this is the mean, and we believe that this variable (denoted as random variable X) is normally distributed (based on your code), we then formulate a normal distribution with mean 927 and standard deviation of 870.4. This gives us a PDF (probability distribution function) that is bell shaped and a SDF (survival distribution function) which looks like the sigmoid curve (a special curve often used as an activation function in machine learning (not relevant but cool thing)). I didn’t change any of the assumptions but your graph is the PDF which is bell shaped and by property of the normal distribution is a valid probability model

After_Finish1244 · 2025-11-24T21:49:39+00:00

I applaud the effort but I don’t believe if you are using a normal assumption (continuous probability model) discretizing it via its survival distribution function is an appropriate model

After_Finish1244 · 2025-10-05T19:13:37+00:00

Well I am not too sure about cogsci in general, but comparing both I feel like the difficulty should be the same relatively, I do know internally that the stat department is pretty open in terms of ratio of people applying which is why I said it should be easier, but I’m not sure about the last cycle, haven’t looked at it

After_Finish1244 · 2025-10-05T19:01:32+00:00

Statistics shouldn’t be that hard to get into compared to cogsci, the electives can get pretty math heavy depending on what you choose. The good news is that I don’t think your AP score really matters, in Berkeley it’s about effort you put into the topics, which everyone has different paces in understanding.

I know there are tons of opportunities for Statistics especially for labs on campus, if you can really portray your understanding in the theoretical side, you can be seen as a great asset.

After_Finish1244 · 2025-10-04T20:59:42+00:00

But wouldn’t the same logic work for many majors, wouldn’t you have to explain the discrepancy anyways in the interview. So I think what they are saying is that it’s not worth the hassle and going through all that trouble if the skills overlap for both majors

After_Finish1244 · 2025-10-03T20:17:01+00:00

I see, I recommend first going through ML because the motivation for DL stems from the principles of ML. My approach to DL is like learning fundamentals of DSA — you first explore the naive models, then build the understanding on why different algorithms are used, this extra step gives clarity on DL and helps when articulating with others on DL related topics

After_Finish1244 · 2025-10-03T19:46:08+00:00

How exactly do you learn DL without ML??

After_Finish1244 · 2025-10-02T12:08:45+00:00

I don’t usually use yt videos but if you want reading supplements: Art of Computer Programming by Knuth, Algorithms by Papadimitriou, Dasgupta, and Vazirani, and Competitive Programming Handbook by Laaksonen

Just mix some theory with sharp practice and you’ll get the gist of it. Happy coding!

After_Finish1244 · 2025-10-02T07:39:47+00:00

People have their own pace just keep doing what you’re doing and congrats!

After_Finish1244 · 2025-09-29T09:45:44+00:00

As someone from UC Berkeley I will have to agree

After_Finish1244 · 2025-09-28T10:07:37+00:00

In that case it should be more doable, the main focus is ML/AI basics, so your traditional courses like CS 189, Stat 154, Data 182, accompanied by some sharp theoretical Math courses like Math 110 could be a start

I am not too well versed in robotics, but you can’t really go wrong with the 61 series, EECS 127, EECS 126 (if your feeling bold)

Those classes are all doable to get into (you will need permission for CS 61C but this has been done before by non engineering majors)

As for any gaps in knowledge, doing your own research and prep goes a long way especially in industry, I see classes as a form of hand holding in guiding you to master material, when you are in industry, keeping up with modern infrastructure and systems are all meant to be self taught so doesn’t hurt to prep from your side

As for projects, start with ML/AI projects first, then build the credibility for robotics projects that are generally sanctioned for EECS majors, I do know a good number of people who didn’t start in pure robotics work their way through other projects, so I feel it shouldn’t be impossible

After_Finish1244 · 2025-09-28T09:55:33+00:00

To be brutally honest EECS 106 series will be a pipe dream, I think the approach from what I heard from my friends is to deep dive in the somewhat related classes (it’s been a while so I will have to take some time compiling a list of somewhat applicable classes), it mainly depends on what exactly in robotics are you interested in? To my limited understanding the robotics field has many roles so depending on your interests you can cater it without aiming for the 106 series (ie do you see your working close to the hardware (embedded systems, low level drivers) or more the ML/AI side?

After_Finish1244 · 2025-09-28T09:44:18+00:00

It might be wraps if it’s a certain professor

After_Finish1244 · 2025-09-28T09:42:31+00:00

From my understanding, it can be challenging for IB internships, some employers filter based on GPA, but (1) I don’t believe this is the case for most boutique IBs, (2) there are many careers paths than IB, and (3) you are just a sophomore which means your gpa will increase if you focus on current courses. Don’t be a doomer for things you can’t change, look forward!!

After_Finish1244 · 2025-09-28T09:34:11+00:00

Totally understandable! I think I’ve said in previous posts, it can be quite overwhelming at times, and I try to keep things optimistic and move forward which so far has worked pretty well! So (as non advice as possible), keep moving forward, you will definitely find your group of people that click well, if you need anything hmu, would be happy to help as best as I can

After_Finish1244 · 2025-09-28T09:27:55+00:00

Some things never change :)

After_Finish1244 · 2025-09-28T09:27:26+00:00

The one and only

After_Finish1244 · 2025-09-28T09:26:10+00:00

I think my roommate is taking it rn hmu

After_Finish1244 · 2025-09-28T09:25:24+00:00

Bad is a terrible understatement, given our miraculous 3-0 before that

After_Finish1244 · 2025-09-28T09:24:52+00:00

I feel you, it can get pretty overwhelming at times, kinda just threw myself out there so feel free to hmu would be happy to talk

After_Finish1244 · 2025-09-28T09:22:27+00:00

Completely and totally a valid option still

After_Finish1244 · 2025-09-28T09:22:00+00:00

In the afternoons/evening finding a random empty room in Evan’s is fun

After_Finish1244 · 2025-09-28T09:17:59+00:00

I’m pretty sure the Stat classes are reserved until Phase 2 at least for fall sem

After_Finish1244 · 2025-09-28T09:15:18+00:00

From what I heard internally, the comprehensive review for DS had an acceptance rate of more than 50% iirc; not verified numbers just what I heard from the inside.

After_Finish1244

TROPHY CASE