Trending Technology Machine Learning, Artificial Intelligent, Block Chain, IoT, DevOps, Data Science

Recent Post

Codecademy Code Foundations

Search This Blog

Introduction to Experimentation and Active Learning in Data Science

  • Data Science and analytics need data (not to mention Big-Data)
  • What if you don't have data
  • Creating Data and analyzing it (sometimes rolled into the same grand problem statement)
  • Online vs Offline context of crating data
  • Online gets covered in Reinforcement Learning
  • In Offline we will discuss Design of Experiments (DOE) and Active Learning
  • Critical difference between observational data and offline experimental data in DOE
Experimental Thinking
  • The operation of system can be conceptualized as a combination of some inputs, which when used together, result in outputs
  • Formal experimentation involves systematic, purposeful changes to input variables in an attempt to gain knowledge about the system and/or find the ideal setting that result in the best output.
Design of Experiments
  • The problems with adaptive One-Factor-At-a-Time (aOFAT)
  • The discrete case
  • Alternative is Orthogonal arrays. An illustration through the Full factorial.

Analysing Designed Experiments
  • Classical Analysis
  • The Take-The-Best Heuristic
          - TTB would have selected A = 1, B = 1, C = 1
  • Where would we use Classical?
         - High error/noise environments can be handled
  • Where would we use TTB?
         - Ultra low error/noise environments
  • The statistical way
         - Use supervised learning technique (stepwise regression is popular)

Sequential Experimentation and Active Learning
  • Sequential Experimentation
  • Active Learning as semi-supervised learning or optimal experimental design
  • Strategies in Active Learning:
        - Uncertainty Sampling
        - Query by committee
        - Expected model change
        - Expected error reduction and variance reduction

No comments:

Post a Comment

Popular Articles