Your Computer Vision course, covering everything from core ideas to current approaches, was what made the field click for me. The way you connected theory to applications helped things fall into place. I'd tell anyone learning from you that they'll walk away with genuine understanding, not just formulas.

It was a great pleasure to be mentored by Arjun while working on our research paper on 3D human pose estimation from monocular images. One thing that stuck with me from his teaching was his advice to fail fast, which is something applicable even beyond research. Given the deluge of information available online now about AI topics, it's essential to find a Guru that can help to cut through the noise and sharpen one's intuition for the subject. For those looking to level up in the path towards mastering AI, I can say with absolute certainty that you're in good hands with Arjun.

Prof. Arjun was my instructor for computer vision course at IIT Bombay, while I was an undergrad there in 2018. Some of the visually intuitive concepts around convolution network's forward pass/backpropagation has stuck with me all these years. He also helped me grasp math heavy traditional CV concepts like Structure from Motion (SfM), which has come in handy many times in my grad research and work after that.

I have been a PhD student working with Dr. Arjun Jain. I also had the opportunity to be a teaching assistant to the computer vision course he taught at IIT Bombay. The one thing that stood out in his teaching is focus on the end objectives while explaining a concept. This would make the lectures much more helpful from an application from point of view. It also helps one to easily follow through the chain of thought without getting overwhelmed by large volume of words.

What stood out to me in Prof. Arjun’s Computer Vision class was the principled way he built ideas, starting from classical vision to modern deep learning architectures, making concepts connect naturally and grounded in theory. One moment that really stuck was when he explained that “everything is a convolution,” even linear layers — it completely changed how I thought about neural networks. I would highly recommend learning from him!

I’ve had the privilege of being Arjun’s PhD student and seeing his knowledge and passion up close for about a decade. I particularly remember his lecture titled, ‘Babysitting the Learning Process’, which presented practical insights that are relevant even today. The new AI age is like rocket science, where details matter, and must be mastered! In this course, you’ll not only learn from a teacher, but also a mentor.

Arjun is a great teacher. He really understands why something is important and is able to convey that instead of diving straight into the nitty-gritties of something. He actually explains the motivation first. It just makes the whole thing way more engrossing, his pedagogy is actually quite application-centric. When you know what something is being used for in the industry that just makes you more motivated to learn and give it your hundred percent.

Just remember how friendly and approachable Arjun and his TA team were. It was also refreshing to learn from someone who came with an industry background and not pure academics. There were also some very unique and well-crafted problem sets during the exams. Also iirc, I got one of my first angel cheque offers from Arjun as well :)

Professor Arjun's teaching gave me an intuitive grasp of Computer Vision that proved essential for my career, especially without a CS degree. As his research assistant, his patience and encouragement to explore unconventional ideas helped me develop a crucial 'feel' for the field, paving the way for my successful research path

Arjun was the first to expose me to CNNs, and it completely changed how I thought about solving problems. I loved how he went deep into architectural details while still keeping things simple enough to understand the broader network design space. He’s incredibly smart and fun to work with, and he taught me to approach data-driven problems from first principles — thinking carefully about data bias, network architecture, and expressivity. If you’re new to AI with pixels, there’s so much you can learn from Arjun.