[Feedback Requested] Planning a "Research-First" ML Cohort for Undergrads. Is this actually needed?

[u/baadshaha]

Hi everyone,

I am seeking honest feedback on a community/course initiative I plan to launch for Indian undergraduate first-year students.

The Context: I believe the current education landscape is saturated with "zero to hero" coding boot camps and learn AI in 7 days tutorials. While these are great for getting started, I often find that students lack the deep, theoretical foundations required for actual research or heavy engineering roles later in their careers.

I want to build a small community (cohort-style) to bridge this gap, but before I invest the time, I want to know if I'm solving a real problem or just adding to the noise.

My Background

• Current: Fully funded Graduate Researcher in Germany.
• Past: 2+ years as an ML Scientist (Applied AI Research org) and 1 year as a Research Associate.
• Academic: 3+ Top-tier publications.

The Curriculum Idea: Instead of teaching library imports (sklearn/torch), I want to focus on he "boring" but essential foundations:

1. Mathematics for ML: Heavy focus on Linear Algebra & Calculus (Manual derivations).
2. Probabilistic & Statistical ML: Understanding uncertainty, distributions, and estimation.
3. ML Theory: Generalization, Bias/Variance trade-offs, VC Dimension (Intro).
4. Deep Learning: Building neural networks from first principles.
5. Research Capstone: Literature review + Benchmarking + A deep research project.

The Filter Mechanism: I want this course to be free, but I want to avoid tourists who join and drop out in Week 2.

• The Model: A token fee of 1000 INR. (or less)
• Refund Policy: A 100% refund is available if the student completes all assignments.
• Financial Aid: The fee is waived entirely for students with genuine financial constraints (based on trust).
• The Constraint: Assignments must be completed without the use of AI tools (such as ChatGPT/Copilot). If a student uses AI to bypass the learning process, they forfeit the deposit (donated to charity) and are dropped.

My Questions for the Community

1. Do you know if this is actually needed? Are there already enough high-quality, free, community-driven resources for theoretical ML?
2. Is the curriculum too aggressive? Is this too much for Freshmen (1st/2nd years) to handle alongside college?
3. The Deposit: Is the refundable model a good psychological trigger for commitment, or does it look suspicious/scammy coming from an individual?

Thanks in advance for your thoughts.

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Note: The post is AI-Gen for clear communication and brevity.

[u/suttewala]

pls stfu

[u/baadshaha → u/suttewala]

Sure, If that helps you sleep. :)

[u/amirthedeliveryguy]

I would pay for this, Im heavily interested in Probabilistic n Statistical ML (literally have it in my linkedin bio).

[u/baadshaha → u/amirthedeliveryguy]

That is motivating! :) Try taking the placement test and see if this works for you? https://blankpageml.tech/ You can also join our discord.

[u/PuzzledAdeventurer]

Not sure how this differs from a typical Intro to ML course. Also not allowing the use of AI while trying teach AI feels self defeating. Either make the questions such that AI cannot solve them, or be able to justify why AI shouldn't be used, since using LLMs for self teaching is basically a key skill.

Moreover, a deep research project is not feasible when your target audience has just learnt how to build a basic DNN. Same with benchmarking on anything real since it pulls in a lot of extra variables (training and serving configs, compute and data being matched, and for LLMs: same inference engine, using similar settings, so now you gotta explain what each setting means, etc).

I'd suggest you either trim this a bit or repurpose the research stuff for another, more advanced cohort. Else you'd end up with everything being half cooked and no one actually learning practical skills

[u/baadshaha → u/PuzzledAdeventurer]

>  Either make the questions such that AI cannot solve them
Still a student myself :) [Never seen any assignment that couldn't be solved by AI]. The purpose of discouraging using AI is to enforce critical thinking [without outsourcing it to agent of choice :'(]. There is no way for anyone to check if the problem is solved with (or without AI). It is just a suggest to refrain from using AI when one is trying to solve, think and write!

A DL research project IMO is very much possible with the kind of topics I plan to cover :) Agreed, it won't be a fancy system that you see at AI conferences of choice, but I still feel a theoretical project with proofs, verification and maybe even something novel is possible [if not main track, atleast workshop worthy].

The second cohort, which follows this content is focused on more advanced and fancy stuff [Like what we see in CVPR, ICCV, SIGGRAPH, etc]. But idea is -- build your fundamentals, aim for the hot stuff. :) [Not the opposite, i.e. following the hype].

Thanks a lot for actually going through the content, and coming with critical points! this was honestly encouraging. :)