This course is an introduction to the major concepts and techniques in the field of artificial intelligence (AI). The material is organized around three significant questions:

• What is a problem and how can a program solve one?

• How can we define a computational intelligence?

• How can such an agent contend with the real world?

Course work blends theory with practice. You will learn to program in LISP so that you can understand the computational challenges AI confronts, and have the opportunity to come up with some clever responses to it. Ultimately, this combination will change forever your ideas about programming and intelligence.

Machine learning is the subfield of artificial intelligence that learns to predict and classify from data. Course material is organized around three essential questions:

• What does it mean for a machine to learn?

• How can we support and evaluate machine learning?

• How do algorithms help machines learn?

This interdisciplinary course takes a pragmatic, hands-on approach to material that is rigorously grounded in mathematics. Course work blends theory with practice. You will learn to use Weka, a powerful, open-source suite of machine learning tools that addresses the theoretical and computational challenges in machine learning. As you explore data from many different sources, this course will change forever your ideas about computers and learning.

SCI 111 is an interdisciplinary science course. It addresses current knowledge about how human brains, human minds, and artificially intelligent machines think. Students will learn about ground breaking work that will provide insight into people, the apps they rely on, and the robots in their future. It is highly recommended for students considering any science major, and for students interested in thinking and problem solving. This course’s material is organized around three essential questions:

• What is in your head?

• How does that make you behave?

• How could we build a machine like that?