6 Years of Studying Machine Learning in 26 Minutes

Summary

This video script narrates the journey of a machine learning enthusiast, detailing their six-year transformation from a computer engineering student to a research scientist at a favorite AI startup. The speaker shares their academic and professional experiences, including initial struggles, pivotal courses, research projects, and the process of landing their dream job. The script also highlights the importance of continuous learning, making informed career choices, and avoiding common beginner mistakes in the field of machine learning.

Takeaways

• 😀 The speaker's journey in machine learning started with an interest in physics and math, leading to a Computer Engineering degree at TU Berlin.
• 📚 The initial years of university were challenging, focusing on foundational courses like linear algebra, calculus, and differential equations, which later became crucial for understanding machine learning.
• 🔧 The speaker's first job as a student researcher in an optical physics lab helped build a strong foundation in experimental work and programming, but also highlighted the importance of seeking new challenges to avoid stagnation.
• 💡 The discovery of AI courses in the fifth semester marked the beginning of the speaker's deep dive into machine learning, starting with reinforcement learning.
• 🎓 The bachelor thesis on deep reinforcement learning for autonomous robotic navigation was a significant step, despite the initial struggles and the steep learning curve.
• 📈 The transition to a Computer Science master's program allowed the speaker to focus exclusively on machine learning, taking advanced courses and engaging in more complex projects.
• 🤖 Working on robotics projects and publishing a paper at a top conference like IROS was a major milestone, showcasing the speaker's growth and capabilities in the field.
• 👨‍🏫 The speaker's experience of switching teams within the research institute to work in the AI department highlights the importance of proactively seeking opportunities to learn and grow.
• 📘 Reading papers and learning from them independently became a habit that contributed to the speaker's development as a researcher.
• 🚀 The pursuit of internships and jobs at top companies, including rejections and learning from them, demonstrated resilience and a commitment to continuous improvement.
• 🌟 The speaker's eventual success in joining a favorite AI startup as a research scientist after years of hard work and learning emphasizes the value of persistence and incremental progress.

Q & A

What was the speaker's initial academic background before getting into machine learning?

The speaker initially studied Computer Engineering at TU Berlin, taking courses in linear algebra, calculus, differential equations, and basic programming in C.

How did the speaker's interest in machine learning begin?

The speaker's interest in machine learning began during their fifth semester when they chose their first AI course, which was split into two parts: old school AI and reinforcement learning.

What was the speaker's first experience with a machine learning project?

The speaker's first machine learning project was their bachelor thesis, where they worked on deep reinforcement learning for autonomous robotic navigation.

What was the speaker's first job as a student researcher, and how did it relate to their later work in AI?

The speaker's first job as a student researcher was at an optical physics lab, running experiments with optical fibers. Although not directly related to AI, it provided them with basic programming skills that later became useful in machine learning.

How did the speaker's experience with coding evolve throughout their studies?

The speaker started coding in C during their computer engineering studies, then moved to Java for data structures and algorithms, and later used Python for machine learning projects, including using libraries like PyTorch.

What is Codium, and how did the speaker use it to improve their coding efficiency?

Codium is a free coding assistance tool similar to GitHub Copilot. The speaker used it to refactor and explain existing code and to write appropriate functions using context from their entire project.

What was the speaker's approach to learning new machine learning concepts?

The speaker learned new machine learning concepts through a combination of university courses, self-study using YouTube videos, reading papers, and hands-on projects.

How did the speaker's job change when they switched teams within the research institute?

The speaker transitioned from working in an optical physics lab to joining the AI department, where they started working on data engineering and gained more experience with machine learning.

What was the speaker's experience with internship applications, and what did they learn from it?

The speaker faced rejections from several top ML internships but learned the importance of persistence and self-improvement. They eventually secured an offer from their favorite AI startup.

What is the speaker's advice for beginner ML students to avoid common mistakes?

The speaker suggests that beginner ML students should watch a follow-up video where they share seven common mistakes to avoid, emphasizing continuous learning and improvement.

What was the speaker's final decision regarding their academic and career path after completing their master's degree?

The speaker decided to join their favorite AI startup as a research scientist instead of pursuing a PhD, although they had an offer for a PhD position.