CourseSpecifications
Valid as from the academic year 2024-2025

Artificial Intelligence (C003756)

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

Course offerings and teaching methods in academic year 2024-2025
A (semester 1)
Dutch
Gent

Lecturers in academic year 2024-2025
Saeys, Yvan

seminarlectureWE02

Offered in the following programmes in 2024-2025
Bachelor of Science in Computer Sciencelecturer-in-charge
crdts
6offeringA

Teaching languages
Dutch
Keywords
Knowledge representation and inference, machine learning, search and heuristics, neuaral
networks and deep learning, natural language processing
Position of the course
Artificial intelligence (AI) is the study of solutions for problems that are difficult or impractical tosolve with traditional methods. It is used pervasively in support of everyday applications suchas email, word-processing and search, as well as in the design and analysis of autonomous
agents that perceive their environment and interact rationally with the environment. The
solutions rely on a broad set of general and specialized knowledge representation schemes,
problem solving mechanisms and learning techniques. They deal with sensing (e.g., speechrecognition, natural language understanding, computer vision), problem-solving (e.g., search,planning), and acting (e.g., robotics) and the architectures needed to support them (e.g.,agents, multi-agents).
The study of Artificial Intelligence prepares the student to determine when an AI approach isappropriate for a given problem, identify the appropriate representation and reasoning
mechanism, and implement and evaluate it.
Contents
Introduction to AI• History of AI• Turing Test• Applications of AI today• Ethical aspects of AISearching and planning• Search problems• Search strategies: uniformed search (DFS, BFS, UCS), Informed search (heuristics, greedy• search, A*), graph search• Adversarial search: adversarial games, minimax, alfa-beta pruning, expectimax• Constraint satisfaction problems:• • Backtracking• • Heuristics• • CSPs and tree search• • Local search• Metaheuristics• • Genetic Algorithms• • Estimation of Distribution algorithms(Approved)1• • Nature inspired search
Knowledge representation and inference• Bayesian networks:• • (conditional) independence• • inference• • d-separation• • Bayesian classifiers• • Markov models• • Hidden Markov models (Viterbi algorithm)• Reinforcement learning
Neural networks• introduction to supervised learning• Backpropagation• Auto-encoders• Deep neural networks• Generative AI
Interacting with the environment• Natural language processing• • Basics of speech recognition• • Hiden Markov Models for ASR• • Transformer models• • Applications of NLP• Explainable AI
Basics of Robotics• Computer vision (basics)• Simultaneous localization and mapping (SLAM)
Initial competences
Starting competences include a good knowledge of data structures and algorithms, basics ofprobability and statistics, programming in Python
Final competences
1 Describe Turing test and the Chinese Room thought experiment. [Familiarity]
2 Differentiate between the concepts of optimal reasoning/behavior and human-like1 reasoning/behavior. [Familiarity]
3 Determine the characteristics of a given problem that an intelligent system must solve.
1 [Assessment]
4 Formulate an efficient problem space for a problem expressed in natural language (e.g.,1 English) in terms of initial and goal states, and operators. [Usage]
5 Describe the role of heuristics and describe the trade-offs among completeness, optimality,
1 time complexity, and space complexity. [Familiarity]
6 Describe the problem of combinatorial explosion of search space and its consequences.
1 [Familiarity]
7 Select and implement an appropriate uninformed search algorithm for a problem, and1 characterize its time and space complexities. [Usage]
8 Select and implement an appropriate informed search algorithm for a problem by designing
1 the necessary heuristic evaluation function. [Usage]
9 Evaluate whether a heuristic for a given problem is admissible/can guarantee optimal
1 solution. [Assessment]
10 Formulate a problem specified in natural language (e.g., English) as a constraint
1 satisfaction problem and implement it using a chronological backtracking algorithm or
1 stochastic local search. [Usage]
11 Compare and contrast basic search issues with game playing issues. [Familiarity]
12 Make a probabilistic inference in a real-world problem using Bayes’ theorem to determine
1 the probability of a hypothesis given evidence. [Usage]
13 Identify examples of classification tasks, including the available input features and output to1 be predicted. [Familiarity]
14 Explain the difference between inductive and deductive learning. [Familiarity]
15 Describe over-fitting in the context of a problem. [Familiarity]
16 Describe how AI techniques can be made more interpretable
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract(Approved)2This course unit cannot be taken via an exam contract
Teaching methods
Seminar, Lecture
Study materialType: Handouts
Name: Lecture material'
Indicative price: Free or paid by faculty
Optional: noAdditional information: Lecture material will be made available through Ufora
References"Artificial Intelligence: A Modern Approach" (3rd ediction) Stuart Russell and Peter
Norvig ISBN-13: 978-0136042594
Course content-related study coaching
Personal contact with the lecturer, by e-mail or by appointment
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment with open-ended questions
Examination methods in case of periodic assessment during the second examination period
Written assessment with open-ended questions
Examination methods in case of permanent assessment
Oral assessment, Skills test, Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible
Calculation of the examination markNiet-periodegebonden evaluatie: individuele opdrachten (40%) + periodegebonden: examen(60%). Om te kunnen slagen voor het opleidingsonderdeel moet een student minstens 10/20
behalen voor de niet-periodegebonden evaluatie. Is aan deze voorwaarde niet voldaan, dankaneen student niet meer dan 8/20 halen voor dit vak.
Indien niet geslaagd voor de niet-periodegebonden evaluatie, kan de student alscompenserende activiteit een projectwerk maken voor de 2e zittijd.

(Approved)3