Blog Posts

🔢 Diving deep into the mathematical foundations that make probabilistic inference possible. This post explores why high-dimensional spaces are so challenging and how Gaussian distributions provide elegant solutions.

From the curse of dimensionality to the beauty of conjugate priors - understanding the theoretical underpinnings of modern AI methods requires grappling with some fundamental mathematical concepts.

Exploring concepts like concentration of measure, the advantages of Gaussian assumptions, and why certain mathematical structures make inference tractable in high-dimensional spaces...

🧠 Welcome to my deep dive into Probabilistic Artificial Intelligence! This course is fundamentally changing how I think about machine learning and AI systems.

It's not just about making machines smart, but about making them humble: systems that know what they don't know, and act cautiously when uncertainty is high.

From Bayesian linear regression to Gaussian processes, from active learning to reinforcement learning - exploring how intelligent agents can reason about uncertainty and make better decisions when the stakes are high...

💽 Welcome to my journey through ETH's Big Data course! This is where theory meets reality, and where I'm learning that handling petabytes of data requires fundamentally rethinking everything.

The core challenge? "We will have to rebuild the entire technology stack, bottom to top, with those same concepts from the past decades, but on a cluster of machines rather than on a single machine."

From the three Vs (Volume, Variety, Velocity) to HDFS and MapReduce, this post covers the foundations of distributed data systems and why we need to completely rethink traditional databases...

"Competitive Programming is a Bad Word" - that's how I started these notes. My self-esteem had decreased by 69% after the first AlgoLab lesson at ETH 😅

This post is my raw, honest journey through the prerequisites for AlgoLab: graphs, algorithms, and data structures. It's filled with code examples, performance tips learned the hard way, and the kind of notes I wish I had when starting out.

From DFS and BFS to Dijkstra's algorithm and the pain of getting TLE errors - it's all here, explained in a way that doesn't make you feel stupid.

Hey everyone! 👋 So you want to know how I made it through my Computer Engineering degree? Well, grab a coffee and let me share some real talk about university life...

The biggest game changer? Finding the right study partner. I was incredibly lucky to find an amazing friend who became my study buddy (and eventually my girlfriend! 😉). We were perfectly complementary - where I had intuition, she had perseverance; where she excelled at perfectionism, I brought different perspectives.

But there's so much more to share about study techniques, the magic of vacation planning during exam season, and those study materials I created that helped tons of students...