Round 11: Machines that learn?


(How do we program a machine to identify zeros and ones in handwritten text?)

Learning objectives

In this round you will learn …

  • … that a computer can learn how to process data (for example, to classify data)

  • … that learning is the process of generalization from examples

  • … that learning is based on identifying regularities in the presented examples

  • … that two main classes of learning techniques are

    • supervised techniques that rely on auxiliary labels associated with the presented examples to articulate the regularity that needs to be learnt

    • unsupervised techniques that work with the presented examples as is, seeking to identify useful features in the data and/or to summarize the data

  • … that teaching a computer is often easier than programming it

    • the machine can learn even if we as programmers do not fully understand the data

    • we can assist by identifying features that expose regularity and make learning easier

  • … that generalization comes with uncertainty and possibility for error

    • perhaps the examples do not cover all relevant phenomena that may occur

  • … to use machine learning techniques to classify simple continuous and discrete data

  • … to assess the ability to generalize through validation

(Material that is marked with one or more asterisks (*) is good-to-know, but not critical to solving the exercises or passing the course.)