CourseSpecifications
Valid as from the academic year 2024-2025

Artificial Intelligence (E016330)

Course size
Credits 6.0(nominal values; actual values may depend on programme)
Study time 180 h

Course offerings in academic year 2024-2025
C (semester 1)
Dutch, English

Gent

Lecturers in academic year 2024-2025
Pizurica, Aleksandra

TW07

lecturer-in-charge

Offered in the following programmes in 2024-2025
Master of Science in Computer Science Engineering

crdts
6offeringC

Teaching languages
English, Dutch
Keywords
knowledge representation, reasoning under uncertainty, Bayesian networks, Hidden Markov
Models, belief propagation, deep learning, rational agents and rational decisions, Markov
decision processes, reinforcement learning, computer vision
Position of the course
The course gives an overview of the principles and modern approaches in artificial intelligence.
The focus is on intelligent agents, reasoning under uncertainty, and making rational decisions.
Contents• Intelligent agents, task environment, agent design (reflex, goal-based, utility-based)• Solving problems by searching: Tree- and graph-search strategies, informed and local• search• Game playing: Game trees, minimax and expectimax strategies• Constraint satisfaction problems• Knowledge representation and reasoning: Logical agents, Resolution, Planning and Acting• Reasoning under uncertainty: Bayesian networks, Markov Random Fields, Inference in• graphical models, Belief propagation, Viterbi algorithm, MCMC samplers• Probabilistic reasoning over time: Hidden Markov Models, Kalman filtering, Dynamic• Bayesian Networks, Particle filtering• Machine Learning: Inductive learning, Naive Bayesian Classifier, Decision Tree Learning,• Artificial Neural Networks• Deep learning: Convolutional neural networks (CNN), autoencoders, deep belief networks• Rational decisions: Utility and preferences, Maximizing expected utility, Value of information,• Decision networks• Making complex decisions: Markov Decision Processes (MDP)• Reinforcement learning: Temporal difference learning, Q-learning, policy search• Advanced topics: applications in machine vision and robotics
Initial competences
Principles of probability theory and basics of predicate logic
Final competences
1 Know and apply search strategies for complex problem solving.
2 Know and apply principles of logic deduction and reasoning, and techniques for action
1 planning.
3 Structure and represent knowledge with predicates, rules, description logic.
4 Know and apply principles of reasoning under uncertainty, using Bayesian networks and1 other graphical models, including Hidden Markov Models and dynamic networks.
(Approved)15 Know and apply basic principles of inductive learning and reasoning.
6 Make rational decisions by combining probability and utility theories.
7 Understand and apply basic principles of reinforcement learning and understand how these1 lead to the design of rational autonomous agents.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Seminar, Lecture
Extra information on the teaching methods
Classroom lectures; Classroom problem solving sessions; Computer-assisted problem solving
Study material
NoneReferences• S. Russel and P. Norvig: Artificial Intelligence, A Modern Approach. Fourth Edition, Prentice• Hall, 2020.
• I. Goodfellow, Y. Bengio and A. Courville: Deep Learning. MIT Press, 2017.
Course content-related study coaching
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment, Assignment
Examination methods in case of periodic assessment during the second examination period
Written assessment, Assignment
Examination methods in case of permanent assessment
Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible in modified form
Extra information on the examination methods
During examination period (PE): written exam, closed book, closed notes except crib sheet on
one A4-size paper.
During semester (NPE): graded practicum reports and homework assignments.
Practicums are organized as group work and involve programming in Python. Homeworkassignments are individual computer exercises. It will not be assumed that you have previous
experience with Python, but you will need to learn the basics rapidly and the necessary support
will be provided for this including an introductory lab session.
Calculation of the examination markWeighting:• Exam: 60%• Practicum reports: 30%• Homework: 10%
In order to be able to pass this course, the minimum score of 9/20 is required both for PE(exam) and NPE (weighted average of the scores for practicum reports and homeworkassignments). If these conditions are not met and the total score is still 10/20 or above, the final
grade will be brought to 9/20. If the exam score is below 10/20, the maximum obtainable final
grade is 10/20 regardless of the total score.
Failing to participate in one or more parts of the evaluation results in the non-passing final
grade.

(Approved)2