JP’s ML Corner

While I am helping to develop polaris, gitlab, I want to collect in this blog post all the good links and information I find about machine learning – computer science’s final frontier.

0 Learning about ML

1 ML Maps

Fig 1.1: Applied Machine Learning Process Overview
Fig 1.2: Machine Learning Algorithm Cheat Sheet – SAS
Fig 1.3: Machine Learning Algorithm Map – Jixta
Fig 1.4: Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data – Medium
Fig 1.5: Data Science Metro Map – Swami Chandrasekaran

2 Algorithm & Documentation

2.1 Testing

3 Classification & Regression

3.1 Classification

3.2 Clustering

3.3 Filtering

3.4 Ranking

3.5 Regression

4 Feature Engineering

  • fets – Feature Extraction Transformers Set fets,gitlab

5 Feature Selection

6 ML Tools

6.1 MLflow

  • MLFlow – Documentation
  • 3 concepts MLflow Doc
    • Tracking
      API and UI for logging parameters, code versions, metrics, and artifacts
      • run: “executes data science code”
      • experiment: organizes multiple runs
    • Projects
      standard format for packaging reusable data science code
    • Models
      offer a convention for packaging machine learning models in multiple flavors

7 Reinforcement Learning

7.x Q Learning

7.x Apprenticeship Learning – Imitation Learning

“Some domains are so complex that it is difficult to define a reward function for use in reinforcement learning.”

“seek additional sources of information about the appropriate reward function. One such source is the behavior of agents who are already optimizing (or, let’s say, nearly optimizing) that reward function—in this case, expert human drivers.
The general field of apprenticeship learning studies the process of learning how to behave well given observations of expert behavior.”

“[A]ssuming the environment is observable, we apply supervised learning to the observed state–action pairs to learn a policy 𝜋(s). This is called imitation learning.” 

Artificial Intelligence: A Modern Approach 22.6

99 Testing