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Table of Contents

  1. 10,000 Hours of Machine Learning.
  2. Structure of the Repository
  3. PORTFOLIO
    1. Projects
  4. Education
  5. PROFICIENCY

10,000 Hours of Machine Learning.

"Machine Learning is just lego for adults" - Dr. Kieran Samuel Owens

"S/he who has a why, can bear almost any how." - Friedrich Nietzche

Why do anything else, when the thing that you could do would help with everything else?

To become an expert at anything, there is a common denominator:

10,000 hours of deliberate practise on the subject.

Structure of the Repository

There are 3 main features:

  1. PORTFOLIO
    • Briefly, these are solutions to classical problems, MNIST, Boston Housing, XOR, etc.
  2. EDUCATION
    • These contain coursework from my university and MOOCs (that which I am allowed to share). Additionally my textbook solutions are included here.
  3. PROFICIENCY
    • These are my more complex and non-trivial projects. They are more fun, but also more novel and thus less deterministic; Kanye West chatbot, Peg Solitare Reinforcement Learner, Ultimate Frisbee Computer Vision, etc.

PORTFOLIO

In no particular order, here are a list of the methods you will find in the notebooks. The emphasis is on understanding their limitations, benefits and constructions.

  • Least Squares Regression
  • Random Forests
  • Boosting, Bagging
  • Ensemble Methods
  • Multilayer Perceptrons
  • Naive Bayes
  • K-means regression
  • K-nearest Neighbours Clustering
  • Logistic Regression
  • Decision Trees
  • SVM
  • Kernel Methods
  • GAN's
  • Stable Diffusion
  • Recurrent Neural Networks
  • Convolutional Neural Networks
  • Transformers
  • word2vec, GLoVE and NLP
  • LLM

Projects

To gain proficiency in all of the above methods, I have solved classical problems that lend themselves well to that particular method:

Dataset Accuracy Model
MNIST A% KNN
FMNIST B% Random Forest
KMNIST C% 2-layer CNN
CIFAR D% CNN
IRIS E% SVM
ImageNet F% ResNet50
Sentiment140 G% LSTM
Boston Housing H% Linear Regression
Wine Quality I% Gradient Boosting
Pima Indians Diabetes J% Decision Tree
IMDB Reviews K% BERT
KDD Cup 1999 L% K-Means Clustering
Digits M% Gaussian Mixture Model
CartPole N% Deep Q-Network

Education

For mastery, a formal education is also required; either by way of open-courseware, or by paying an institution.

I have done both, and overall benefitted as a result.

  • UNSW AI
  • UNSW Machine Learning and Data Mining
  • UNSW Deep Learning and Neural Networks
  • UNSW Computer Vision
  • Stanford CS229 (Machine Learning)
  • Stanford CS230 (Deep Learning)
  • Mathematics for Machine Learning, Ong et al.
  • HOML (Hands on Machine Learning)
  • All of Statistics, Larry Wasserman
  • Coursera Machine Learning Specialisation
  • Coursera Deep Learning Specialisation

PROFICIENCY

To become proficient, I have applied my ML skills to solve problems of personal and social interest.

  • Kanye West chatbot
  • KiTS19 Grand Challenge: Kidney and Kidney Tumour Segmentation
  • Non-descriptive Ultimate Frisbee Statistics
  • OCR
  • Peg Solitaire RL

"Read 2 papers a week" - Andrew Ng