Nithin Bekal

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Machine Learning

Machine learning day - lecture notes

Notes from Praseed Pai’s lecture, Machine learning day - KMUG (Praseed Pai) - 9-JUL-2016

  • Analytical thinking vs system thinking
    • analytical
      • break problem down and solve
    • system thinking
      • holistic approach, nonlinear
      • assume dependent vars
  • Algorithmic techniques

    • Hilbert space methods
      • proximity queries between datasets
      • Hilbert’s 23 problems (esp. pbm #2 and #10)
    • statistical learning
      • types of statistics
        • non parametric statistics
          • categorical/nominal data
          • ordinal data (signifies order)
        • parametric
          • ratio
          • interval
      • descriptive
        • central tendency
        • dispersion
        • association
    • deep learning
      • neural networks
  • Algorithmic classification

    • supervised learning
      • classification
      • regression/prediction
        • classification based on numerical data
    • unsupervised
      • clustering
      • dimensionality reduction
    • association analysis
      • apriori
        • eg. Given historical retail data, decide whether customers who purchase bread and sugar should be offered a coupon for another product, say beer. We can solve this by finding the % of baskets that have beer in addn. to bread and sugar.

        • P(Y X) - prob of Y given X.
            TxnID  | items
          ---------+--------------------
            1      | shoes, shirt, jacket
            2      | shoes, jacket
            3      | shoes, jeans
            4      | shirt, sweatshirt
          
            items           | Frequency
          ------------------+----------
            shoes           | 75%
            shirt           | 50%
            {shoes, jacket} | 50%
          
  • Decision tree classifier
    • generate decision tree based on inputs
  • Naive Bayes
    • initial condition - priori probability
    • adjust probability based on new data
    • assumes independent variables
    • posterior probability - calculate based on priori data
    • eg. given learning data height, weight, foot size, predict gender.
    • read up:
      • false positive % - sensitivity
      • false negative % - specificity
      • base rate
      • monty hall problem
      • Weka - ML tool

MOOCs

  • Caltech ML course by yasser abu mustafa
  • Weka MOOC

Recommended books

  • Machine Learning (Tom Mitchell)
  • Statistics hacks (Bruce Frey)
  • Financial Numerical Recipes in C++ (available online)
  • Data Science for Dummies Using Python
  • Machine Learning with Scikit Learn - Packt