October 26, 2026
Hi all,
I wanted to post a blog, I had to submit my first set of assignments. I'm still on track for my 1:1 as I stated in a previous blog. I wanted to come on and talk about some things I've learned.
Firstly, I'm taking modules in ML, Programming for AI and Statistics.
I've really gotten my grips on Python. I'm feeling confident in it but I'm still catching up on material.
We're just started week 8 which is 3/4 through the semester. I'm using the Feynman Study Method to learn difficult topics. This is the simplified 4 step process that really encourages self-learning.
1. Pick the topic you want to learn. 2. Read about it and then explain the concept in a way you understand it ( or ELI5). Explain it out loud with no jargon. 3. Identify the learning gaps from exercise 2, re-read the material and solidify your understanding, using analogies and asking yourself how it relates to what you're learning, and why you need to know it. 4. Repeat the teaching a child exercise.
Step 3 really focuses on recursive learning. This has been key in my understanding of ML principles and some of the programming methodologies, especially in recursive programming and in understanding OOP.
After this is completed, the concept should come to you on an intuitive basis.
Following up on this, I find some sample questions from the textbooks or I use an LLM such as Gemini's Notebooks to generate questions that test my understanding.
I then correct my own work from the book, improve the answer (if needed) and ask the LLM to grade me.
It's a time consuming process, but it works.
Thankfully I have access to the lecture materials on-demand but I have gone back to writing notes like I did in secondary school and it has proved immensely beneficial for my memory retention. I briefly used Obsidian for note taking and organisation, but I just prefer the refill pads and hardbacks!
I used a Google Colab (and a Jupyter Notebook) for my ML assignment. I'm a big fan, it helped me troubleshoot really quickly and it also really helped my understanding in the R syntax.
I implemented a Decision Tree Classifier and the K-Nearest Neighbours algorithm.
I learned learned about recursion, dicts and using OOP to recreate my own dict.
In statistics, I learned the fundamentals of populations and samples, types of studies (experimental, oberstational etc.), study design and about discrete and continuous distribution. (Bernoulli Trials, Binomial and Poisson distribution).
I miss when I only had to worry about a small number of symbols and mainly numbers and regular letters in secondary school math haha.
I'm still catching up on material but I hope to be up to speed next week.
The masters is tough for sure. But I'm really happy that I'm pursuing it.
I've put my independent projects on the sideline while I focus on work and study (hence less frequent blogs).
I made a pomodoro timer for my laptop and I'm looking at converting my Canvas Buddy project (GitHub) to have a RAG implementation, so I can shoot it a message on Discord to retrieve information I'm looking for ( ie due dates)
Here's a nice video talking about the impact of learning and suffering trials / doing something meaningful.
Best,
Greg