Official course description, subject to change:
Basic info last published 1/10-23

Introduction to Artificial Intelligence, MSc

Course info
Language:
English
ECTS points:
7.5
Course code:
KSINARI1KU
Participants max:
85
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
yes
Price for EU/EEA citizens (Single Subject):
10625 DKK
Programme
Level:
MSc. Master
Programme:
MSc in Software Design
Staff
Course manager
Rune Møller Jensen
Associate Professor
Course semester
Semester
Forår 2024
Start
29 January 2024
End
23 August 2024
Exam
Abstract
The overall goal of the course is to introduce students to a selection of the most important problem solving and decision support techniques within AI and optimization.
Description

Computers are increasingly used to support decision making, exploiting that computers can perform computations at scale, on large amounts of data, requiring large amounts of computation steps, or both. This course studies how to solve such decision problems on computers. 
The goal is to make students able to identify, design, and implement efficient solutions to the kind of decision problems that arise in modern organizations and IT products. The expectation is that a student mastering the material is able to work in internationally leveled business intelligence and optimization groups as well as in development departments of “intelligent” applications as used in smart phones, computer games, enterprise resource planning systems, and decisions support systems. 
The course will cover the followings topics:

Search algorithms
  • Informed search: greedy heuristic search, A*, breadth-first heuristic search
  • Local search: hill-climbing, simulated annealing, genetic algorithms, tabu search, population-based search
  • Adversarial search: Minimax search, alpha-beta pruning
Reinforcement learning
  • Markov Decision Processes
  • Q-learning
Propositional logic
  • Representations: disjunctive (DNF), conjunctive (CNF), and if-then-else (INF) normal forms, Binary Decision Diagrams (BDDs)
  • Reasoning: resolution, SAT-checking
Constraint programming
  • Local consistency: arc-consistency, path-consistency, i-consistency
  • Look-ahead search strategies: forward-checking, arc-consistency look-ahead, maintaining arc-consistency
Linear Programming
  • Simplex algorithm
  • Duality
Formal prerequisites

  • You must have passed an elementary programming course. (for example
  • Introductory Programming)
  • You must have passed a discrete mathematics course (for example Foundations of Computing, Discrete Mathematics)
  • You must follow in parallel, or have passed an introductory algorithms course (for example Algorithms and Data Structures).

If you are an external student, it is important that you have programming-experience from elsewhere, i.e. through a daily use in a developer position in the software industry.
Intended learning outcomes

After the course, the student should be able to:

  • Identify decision problems in work processes and IT products that can be solved by AI and optimization algorithms.
  • Implement AI and optimization software components to solve these problems efficiently.
  • Apply standard AI and optimization models and solvers.
  • Participate in concept development of advanced decision support systems.
Ordinary exam
Exam type:
A: Written exam on premises, External (7-point scale)
Exam variation:
A22: Written exam on premises with restrictions.