1. Crash course in probability theory and
statistics – part 1
Machine Learning, Mon Apr 14, 2008

2. Motivation
Problem: To avoid relying on “magic” we need
mathematics. For machine learning we need to
quantify:
●Uncertainty in data measures and conclusions
●“Goodness” of model (when confronted with data)
●Expected error and expected success rates
●...and many similar quantities...

3. Motivation
Problem: To avoid relying on “magic” we need
mathematics. For machine learning we need to
quantify:
●Uncertainty in data measures and conclusions
●“Goodness” of model (when confronted with data)
●Expected error and expected success rates
●...and many similar quantities...
Probability theory: Mathematical modeling when
uncertainty or randomness is present.
P  X = x i , Y = y j = pij