Week 2

Week 2: Learning machine learning

This week we tackle the myth of “thinking machines” by focusing on the branch of AI undergoing most development and use today: machine learning (sometimes abbreviated to ML). We will give a high-level overview of the process by which machines are said to learn from inputs and produce meaningful outputs. We consider how different problems are designed to fit the logic of different machine learning techniques, using examples ranging from linear regression to deep learning. 

Required Readings 

  • Chapter 2: MACHINE LEARNING, STATISTICS, AND DATA ANALYTICS (p. 35-69) in Alpaydin, Ethem. Machine Learning : the New AI. Cambridge, Massachusetts: The MIT Press, 2016. Print. (Available on the reading list).

Further reading/additional resources

  • Hidalgo , C. “Machines don’t think, but neither do people” in What to Think About Machines That Think : Today’s Leading Thinkers on the Age of Machine Intelligence. First edition. New York: Harper Perennial, 2015. Print. (Available from the library).


[[content/slides/7AAVCD42_w2/index|Week 2 slides]]