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COSE361 · Teaching

Artificial Intelligence

Course information

What is this course about?

This course aims to develop a solid understanding of core concepts in artificial intelligence, including machine learning, search algorithms, Markov decision processes, and probabilistic reasoning, and to cultivate the ability to apply these concepts to real-world problem solving.

Students will also learn to analyze the underlying mechanisms of various AI models, select and implement algorithms appropriate to given problem settings, and interpret results with a practical, problem-oriented mindset.

Resources

There is no required textbook for this class. Slides are mostly self-contained. These books may be used for further understanding.

  • Russell and Norvig, Artificial Intelligence: A Modern Approach
  • Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
  • Sutton and Barto, Reinforcement Learning: An Introduction

Prerequisites

  • Familiarity with basic programming (Python 3)
  • Familiarity with basic probability theory
  • Familiarity with basic linear algebra

Grading

  • Attendance: 10%
  • Programming assignments: 10%
  • Midterm exams: 40%
  • Final exams: 40%

Up to five absences will have no penalties. Each absence beyond five will result in a 1% deduction.

Schedule (subject to changes)

Open in Google

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