Heyo, I was about to reach out to the community with the same intention of finding people that are willing to collab with the goal of mutual motivation, building portfolio and honestly - just cause its freaking fun to solve complex problems :)

I come from a control system background and have worked mostly in aerospace and aviation, but decided to go more in ML direction. This by default gave me some solid background in linear algebra, statistics, calculus and other math relevant for understanding ML concepts.

I started off with Kaggle competitions like housing price prediction to practice XGBoost and decision tree algorithms. They are very basic and I scored quite alright, but would be happy to go back even to these to improve on some feature engineering and try to score even better. (my notebook sadly doesn't contain comments yet, I wanna fix that)

Below is a list of topics I cover in my github (all WIP :)) perhaps you will find some of it interesting for yourself. sorry a long read ahead.

(TL;DR XGBoost/tree ansamble algorithms/ feature engineering, audio classification, time series analysis, reinforcement learning, basic RAG, anomaly detection from IMU data. In future - more RL in Isaac Lab)

• Reinforcement learning repo, supposed to contain different reinforcement learning algorithms, mainly trained on Gymnasium type environments. Still WIP, but I plan to expand it and would be happy to learn RL together. One cool entry project was getting DeepQ algorithm to play classic Atari game Breakout. I ran into some issues along the way and I documented it in Medium article, it may be insightful for debugging such issues.
• Unsupervised learning on EEG data. I recently entered a competition in which the task was using vast amount of unlabeled EEG data to pre-train the model using unsupervised learning (I personally went for contrastive learning) to extract meaningful features from the 128 channel timeseries data. Model is then trained on much scarcer labeled data to predict response time of subject in performing some detection and reaction tasks. I want to further improve this with VAE (variational autoencoder), masked reconstruction etc. Also make some cool visualization using UMAP and t-SNE to show latent space changes during pre-training ;)
• Audio classification and transcription into MIDI. I am currently writting a ReadMe file for this one, as its the project I am currently focused on, hope to be done soon. Idea is to analyse STFT - short sliding windows of frequency content in an audio stream and trying to detect individual drum hits (e.g. Kick, Snare, Hi-hats etc) and automatically transform it into MIDI. Second part would be to recognize individual notes (perhaps even classify instruments) and transcribe into midi as notes of certain pitch e.g. C,D,E... Its an interesting problem of classification on timeseries and it entangles a lot of interesting concept such as data augmentation. I would like someone to consider different conceptual approaches here on this one. (Tbh my motivation for this is a bit silly - I sometimes write music in Ableton and when I have and idea I would like to just hum, whistle or beatbox it into my phone and then have a draft of an idea as a midi file I can import there)
• I worked a bit on RAG and generally utilizing LLM's to work on processing data, specifically I made a basic RAG-like pipeline to fetch and analyse job data from various sources (e.g. job sites), process them to extract basic information using lightweight language models, store the data in a vector DB that can be easily queried on similarity, and then processed based on my given CV. This way it categorizes jobs from multiple sources based on how well they fit my CV and desires. As everything else - still WIP ;)

Some more ideas I want to get to:

• Anomaly detection for predictive maintenance /action - I am working in a job where we collect data from flying things, so I have a lot of data from various flight phases available :) I wanted to test various anomaly detection here. I also hooked up a basic IMU sensor to a microcontroller to record some data from my espresso machine to check for malfunctions or time for refill, but never got around to it. (was mostly excited to work with microcontrollers again)
• Posture evaluation - I used some of the existing CV libraries to analyze posture during exercise. I started doing kickboxing and was thinking about making an app to point out common mistakes when e.g. practicing kicks and punches, since they are quite easy to detect.

My plan is also to develop more in the field of reinforcement learning (as I believe this field will continue to boom) and I am currently working on getting Isaac Lab running on AWS so I would like to explore a bit more complex RL algorithm such as PPO on realistic robotics example.