Warrick's Machine Learning Notebook

Hi! Warrick here.

This documents my journey to learning ML.

This will be a combination of algorithms learned from both my course work and side projects.

So far there has been 15 entries.

Introduction

1. Supervised and Unsupervised Learning!

ML Basics (Complete)

1. Decision Tree + Random Forest from Scratch (Complete)

2. Linear Regression (Complete)

3. k-Nearest Neighbors (Complete)

4. Logistic Regression (Complete)

5. Naive Bayes (Complete)

6. k-Means Clustering (Complete)

Deep Learning

1. Artificial Neural Networks (Complete)

2. Convolutional Neural Networks (Complete)

3. Encoder-Decoder Architectures (Complete)

4. RNNs, LSTMs, and GRUs (Complete)

5. Transformers (Complete)

6. GANs (Complete)

7. Graph Neural Networks (Complete)

Reinforcement Learning

1. Basics: State, Action, Value Functions

2. Monte Carlo Policy Evaluation

3. SARSA

4. Q-Learning

5. RL-Squared (Y. Duan et al)

6. PPO (Proximal Policy Optimization)