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This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

Table of Contents

  1. Watch Lecture 0 on or after Mon 1/27.
  2. Complete Quiz 0 on or before .
  3. Attend section.
  4. Optionally attend office hours.
  5. Work on Project 0, due .

Knowledge

  1. Watch Lecture 1 on or after Mon 2/10.
  2. Complete Quiz 1 on or before .
  3. Attend section.
  4. Optionally attend office hours.
  5. Work on Project 1, due .

Uncertainty

  1. Watch Lecture 2 on or after Mon 2/24.
  2. Complete Quiz 2 on or before .
  3. Attend section.
  4. Optionally attend office hours.
  5. Work on Project 2, due .

Optimization

  1. Watch Lecture 3 on or after Mon 3/9.
  2. Complete Quiz 3 on or before .
  3. Attend section.
  4. Optionally attend office hours.
  5. Work on Project 3, due .

Learning

  1. Watch Lecture 4 on or after Mon 3/23.
  2. Complete Quiz 4 on or before .
  3. Attend section.
  4. Optionally attend office hours.
  5. Work on Project 4, due .

Neural Networks

  1. Watch Lecture 5 on or after Mon 4/6.
  2. Complete Quiz 5 on or before .
  3. Attend section.
  4. Optionally attend office hours.
  5. Work on Project 5, due .

Language

  1. Watch Lecture 6 on or after Mon 4/20.
  2. Complete Quiz 6 on or before .
  3. Attend section.
  4. Optionally attend office hours.
  5. Work on Project 6, due .

Test

  1. Complete the Test on or before .