Monthly Archives: May 2017

Algorithm Independent Aspects of Machine Learning

Note: this post meant to help clarify the tutorial questions (number 1, 3, 4, 5)  for COMP 9417 – Week 11, School of Computer Science and Engineering, UNSW (s1 – 2017)

Regardless of machine learning algorithm that we choose, we still may want to know about these stuffs (slide – page 6):

  • How many training examples a learner should have before it converges to correct hypothesis? (sample complexity)
  • How large is a hypothesis space? How complex is a learner’s hypothesis? (hypothesis complexity)
  • How many errors a learner are allowed to misclassify before it finally converges to a successful hypothesis? (mistake bounds)

Let’s take a look at first aspect. Continue reading