Round 10: 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.)