Four years at NITK Surathkal, one of India's top engineering schools. I arrived with no idea about coding (fun fact, I started off as an Electronics and Communications student). But by the end of 4 years, I was in love with intelligent systems, and coding.
Finding Direction
The first year passed by in just discovering my classrooms XD But I had a sense of what I want to study, and what I had to pursue forward. I joined the bachelor's as a
The second year was all about building foundations. Data structures, algorithms, discrete math, computer architecture. At the time, some of it felt abstract. Later, when I was debugging production ML systems or optimizing inference pipelines, I kept reaching back to concepts from those early courses.
What I didn't expect was how much the breadth would matter. Operating systems explained why certain optimizations work. Database theory clarified how to structure data for ML. Networks helped me understand distributed training. The curriculum built a mental model of how computers actually work, layer by layer.
Discovering ML
The machine learning courses changed my trajectory. I'd written some classification models before, but formal coursework gave me the theory. Understanding the math behind gradient descent, regularization, and probability made algorithms feel less like magic and more like engineering.
I started spending extra time on ML projects, going beyond assignments to experiment with architectures and datasets. The combination of theory and tinkering was addictive.
First Research
My senior year, I joined a research project on medical image analysis. We worked on automated prostate cancer grading using deep neural networks. The project meant collaborating with pathologists, learning what features they look for in tissue samples, and designing models that could identify similar patterns.
Publishing that work was my first taste of research. The process taught me things coursework couldn't: how to frame questions, design experiments, handle peer review, and communicate technical results to non-technical collaborators.
What I Took Away
I graduated with honors and a 9.4 GPA, but the degree was less important than what I learned to do. NITK gave me strong fundamentals, introduced me to machine learning, and showed me that research was something I could pursue.
Most importantly, it taught me how to learn. New frameworks and techniques come and go. The ability to pick up unfamiliar concepts quickly, find the right resources, and build working understanding from scratch, that's the skill that lasts.