Series

Notes I wrote while working through a course.

OMSCS Course Notes

5 parts so far

What I want to remember from each semester of Georgia Tech's OMSCS program.

  1. 1 ML4T: Machine Learning for Trading
  2. 2 AI4R: Artificial Intelligence for Robotics
  3. 3 SDP: Software Development Process
  4. 4 KBAI: Knowledge-Based AI
  5. 5 AIES: AI, Ethics, and Society

Machine Learning (Stanford)

8 parts

Notes from Andrew Ng's ML specialization. Linear and logistic regression, neural networks, decision trees, and unsupervised methods.

  1. 1 Introduction to Machine Learning
  2. 2 Regression with Multiple Input Variables
  3. 3 Logistic Regression: From Sigmoid to Regularization
  4. 4 Understanding Neural Networks: From Biology to TensorFlow
  5. 5 Training Neural Networks: Activation Functions, Backpropagation, and TensorFlow Implementation
  6. 6 Practical Advice for Applying Machine Learning
  7. 7 Decision Trees and Ensembles: From Splits to Random Forests and XGBoost
  8. 8 Unsupervised Learning: K-means Clustering and Anomaly Detection