Why I Want to Join This Course: My Journey with Physics and Data

I still clearly remember the first time I watched Andrew Ng’s Machine Learning Specialization on YouTube. It was my first introduction to the world of data, and from that moment, I was hooked. That’s how I started looking deeper into this field. It wasn’t just the exciting world of machine learning that caught my attention, but also how it opened my eyes to something deeper: the fusion of physics and data.

I’ve always thought of physics as a field where analytical equations and theoretical models dominate. However, that course showed me something amazing — with raw data, we can turn these theories into practical, data-driven insights that offer new perspectives on the laws of the universe.

Solving A tutorial on solving ordinary differential equations using Python and hybrid physics-informed neural network — ScienceDirectODE

What was even more interesting was that I found that many AI figures, including Andrew Ng himself, came from a physics background! This connection between physics and AI showed me that the two disciplines, while different, share a common link: the attempt to understand complex systems

Influential AI leaders with backgrounds in physics: Andrew Ng (left), Andrej Karpathy (Top Right), Fei-Fei Li (Bottom Right)

What I Hope to Learn

Through this course, I aim to gain both foundational and advanced knowledge that can serve me in multiple capacities. Here’s a breakdown of the key skills I want to develop:

• Understanding Generators
  In computational contexts, generators often deal with creating iterative processes or synthetic data. I see this as an important skill, particularly in physics, where simulations can provide insights into phenomena that are difficult (or impossible) to observe directly. I want to learn how to develop efficient data generators that can simulate real-world conditions, such as energy systems or material behavior under various forces.
• Modeling Real-World Systems
  My long-term goal is to work on sustainable energy solutions, quantum machine learning for materials and modeling plays a critical role here. By mastering mathematical and computational modeling, I hope to create more accurate representations of complex physical systems, such as photovoltaic cells or battery storage systems. With better models, we can optimize renewable energy solutions to be more efficient and cost-effective, a step toward combating climate change.

Dipole Source 3-D Wave Simulation (falstad.com)

• Data Analysis
  Data is only as useful as our ability to analyze it. I want to dive deep into statistical methods and machine learning algorithms that can help me extract meaningful insights from massive datasets. Whether I’m working with experimental data in a lab or analyzing trends in global energy consumption, having the ability to make sense of that information will be crucial.
• Programming Proficiency
  Since data analysis often requires strong programming skills, I am eager to enhance my coding abilities, particularly in Python and R, which I’ve already explored in machine learning contexts. I believe that combining coding with physics will enable me to build my own tools for both theoretical and experimental work.

The Bigger Picture

Ultimately, this course is not just about checking off another requirement on my academic list; it’s a stepping stone toward larger goals. I envision using the skills I’ll develop here to contribute to real-world innovations, particularly in renewable energy technologies. With a growing global emphasis on sustainability, the ability to model, generate data, and analyze complex systems will be indispensable.

But my ambitions don’t stop there. Beyond renewable energy, I am also deeply intrigued by the potential of Quantum Machine Learning and how it can be applied to material simulation. This area holds enormous promise for improving the design and efficiency of materials used in energy storage and generation. And as a side quest, I hope to one day apply these advanced data skills in the world of finance, exploring how quantum computing and machine learning can offer new solutions for complex financial modeling and risk analysis.

By the end of this course, I hope to have not only a strong grasp of generators, modeling, and data analysis but also the confidence to apply these skills in practical settings. Whether it’s improving the efficiency of solar panels, optimizing energy storage, or even contributing to advancements in quantum machine learning.

I am excited to embark on this journey and see where it leads. One thing is certain: the future will be shaped by data, and I’m eager to play my part in that future!