Machine Learning

This is the repository for the course Machine Learning at Asian Institute of Technology.

Corresponding YT: https://www.youtube.com/watch?v=KnP5n8TSJc4&list=PLqL-7eLmqd9ViCe07M6WiCVyaWJRc_plF

Prerequisites

Visit our "Prerequisites" folder to review the materials, before attempting our ML course

Technology stack

NumPy, Pandas, Matplotlib, Sklearn, PyTorch - for machine and deep learning
MLFlow - for experimenting
FastAPI - for exposing the models
Anything for frontend, e.g., Vue, ReAct, Angular, Jinja, Hugo, etc.
Anything for backend, e.g., Django, Flask
Docker for containerization, and Traefik for reverse proxy

Outline

The course is structured into 5 big components:

0. Case Study

Regression
Classification

1. Supervised Learning

Regression

Gradient Descent
Stochastic and Mini-batch
Regularization

Classification

Logistic Regression
Naive Bayes
K-Nearest Neighbors
Support Vector Machines
Decision Trees
Random Forest
AdaBoost
Gradient Boosting

2. Unsupervised Learning

K-mean clustering
Gaussian mixture
Principle component analysis

3. Deep Learning

Feedforward Neural Netork
Convolutional Neural Network
Recurrent Neural Network

4. Reinforcement Learning

PPO

References:

Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2016 (2nd edition) (https://web.stanford.edu/~hastie/)
Ian Goodfellow., Deep Learning, 2016
Pytorch tutorials available online: https://pytorch.org/tutorials/
Nice visuals on CNN - https://github.com/vdumoulin/conv_arithmetic