Machine learning day  lecture notes
Notes from Praseed Pai’s lecture, Machine learning day  KMUG (Praseed Pai)  9JUL2016
 Analytical thinking vs system thinking
 analytical
 break problem down and solve
 system thinking
 holistic approach, nonlinear
 assume dependent vars
 analytical

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
 non parametric statistics
 descriptive
 central tendency
 dispersion
 association
 types of statistics
 deep learning
 neural networks
 Hilbert space methods

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%

 apriori
 supervised learning
 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