This course is not about learning how to use machine learning tools—it’s about learning how to think like a machine learning engineer.

At its core, machine learning is the art and science of modeling the world through data; a lot like how humans do it. We live in a world that is infinitely complex, and our task is to compress that complexity into useful, generalizable representations, that take the form of math, language, and algorithms.

Rather than focusing on toolkits or memorizing algorithms, you’ll develop the ability to ask sharp questions, evaluate model behavior, and generate your own solutions. The goal is to build your intuition, fluency, and creative confidence in modeling systems from raw observations; preparing you to reason at the same level as the intelligent systems now emerging around us.

General Learning Guidelines

The main purpose of this course is to get you into the mindset of a machine learning engineer who either has to use or write algorithms to deal with datasets. It’ll give you the foundation you need, whether you’re planning to contribute to the advancement of the field, or you’re going to use it to solve practical real-world problems. There are very reliable ways to get better at that:

• Code, code, code: get comfortable with programming. Explore libraries or languages that can help you solve your problem. Machine learning is a quickly evolving field, and no matter how much theory you know, you will need to keep up with new tools. For that I’ll give you coding assignments, and I’ll try to make them as fun as possible. They might be challenging sometimes, and this is how you learn.

• Read and write: the best way to continue learning after you complete this course is to keep up with the literature through reading, and to write about what you learn and build. Write down your project ideas and don’t settle on the first idea that comes to mind. This is how you develop your creativity and your ability to communicate your ideas: these skills are always under-emphasized in STEM education, and you’ll find that much of your job will turn into reading and writing. This is the purpose of the progress and final project reports.

• Communicate and share: the best way to learn is to teach. I encourage you to discuss the course material with your colleagues, and to ask questions. You’ll present your final project through a poster session. This is a good opportunity to practice your presentation skills, and to learn from your colleagues.

You will be provided with a list of suggested datasets to which they have to apply concepts learned in the course. A progress report on the 9th week with completed milestones of the course is required. Graduate students are encouraged to apply machine learning tools to data collected from their own research projects.

Online Resources and LLM Policy

Large Language Models (such as ChatGPT) are not banned, but I recommend using them with extreme caution. I believe that you can only learn by getting exposed to as many problems as possible and deeply thinking about them. When you solve problems, your brain tries multiple routes, failing and learning through trial and error until it becomes good at connecting and building complex ideas. This is how your mind becomes both sharper and more creative.

If you always use a solution manual, an LLM (as a smart solution manual), or equivalently a smart friend, you basically learn to become obsolete. So every time you use an LLM, I want you to notice who is serving who. Are you learning to become an assistant to the AI, or is the AI helping you become better at solving problems? If it doesn’t feel like you’re putting in the effort, and there’s no sweat involved, then it’s probably the former.

So, for the homework, my recommendation would be: read the question, try to solve it on your own as much as you can, write down your solution, ideas and questions. If you can’t solve it, discuss it with friends. LLMs, search engines and good-old books are actually great tools for exploration and learning. If you know how to use them, you don’t need me. But nothing can replace the effort of solving problems on your own.

Course plan and logistics

• Course website: I will use this website (www.intro2ml.com) to post the course materials, assignments, and other resources. The purpose is to make it a reference for you that you can come back to even after the course is over.
• Slack: we will use Slack for communication and discussions. Some assignments will be posted there. If you are not on Slack yet, send me an email to get an invite.
• Course materials: I will post the course material on the schedule page.