Notes:Algorithms for Intelligent Decision Making

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Personal notes for this course

General info

Course description Decision making is at the centre of artificial intelligence. This course gives you practical skills on a solid theoretical base:

  • Modelling realistic combinatorial optimisation problems involving multiple users, and designing systems to solve such problems.
  • Emphasis on problem characteristics seen in decision problems in energy, logistics, and health sectors.
  • Mathematically-grounded techniques with computational feasibility: sequential decision making (reinforcement learning), algorithmic game theory, constraint programming.

Apply the skills you learn in this course by taking CS4210-B: Intelligent Decision Making Project in quarter 4!

Expected prior knowledge Recommended: IN4301: Advanced Algorithms, or equivalent; and/or IN4010: Artificial Intelligence Techniques, or equivalent

Required: basic course(s) in algorithm design and analysis, and complexity theory

Lectures, homework exercises (optional), and programming assignments.

The expected workload is:

  • 30% lectures (including preparation for the exams)
  • 40% homework exercises (optional)
  • 30% programming assignments


  • Open book exam
  • Programming assignment