Machine Learning Weekly Class Note

November 18th, 2018

What is Artificial Intelligence?

  • “Models for Thinking, Perception, Action” — Patrick Winston, MIT
  • AI is the automation of thought @fchollet
  • A field of study that gives computers the ability to learn without being explicitly programmed.” Arthur Samuel 


Types of Machine Learning

  • Rule-based Systems
  • Supervised Learning
    • Classification, Regression
  • Unsupervised Learning
    • Clustering
  • Reinforcement Learning
  • Generative output
  • Deep Learning – Machine learning with “deep” neural networks



  • Sample – a set of data collected and/or selected from a statistical population by a defined procedure.
  • Feature – an individual measurable property or characteristic of a phenomenon being observed.
  • Label – inferring a function from labeled training data.
  • Prediction – a rigorous, often quantitative, statement, forecasting what would happen under specific conditions
  • Cost / loss – a function that maps an event or values of one or more variables onto a real number intuitively representing some “cost” associated with the event
  • Training – to optimize some measure of performance such as minimizing the number of mistakes made on new samples
  • Training set, test set, validation set – a set of examples used to fit the parameters
  • Model – building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions
  • Learning rate (step size) aka Gradient Descent – first-order iterative optimization algorithm for finding the minimum of a function



  • Big-O notation- quantifies the amount of time taken by an algorithm to run as a function of the length of the string representing the input.
  • Binary Search Tree – Binary search compares the target value to the middle element of the array; if they are unequal, the half in which the target cannot lie is eliminated and the search continues on the remaining half until it is successful. If the search ends with the remaining half being empty, the target is not in the array.
  • Breadth-First Search – traversing or searching tree or graph data structures
  • Depth-First Search – explores as far as possible along each branch before backtracking
  • Dijkstra’s Algorithm – an algorithm for finding the shortest paths between nodes in a graph
  • A* search – plotting an efficiently directed path between multiple points, called nodes






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