Summary
Samenvatting AI Programming
In dit document staat de hele curus over AI Programming samengevat. Het gaat over algemene zaken zoals AI en machine learning tot specifieke zoekalgoritmen.
Enkele behandelde onderwerpen: agents, POAS, zoekalgoritmes (local & adverserial), minimax, native bayes, machine learning, k-means cluster...
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Uploaded on
October 3, 2022
Number of pages
14
Written in
2020/2021
Type
Summary
Institution
Artesis Plantijn Hogeschool Antwerpen (Artesis)
Education
Elektronica-ICT
Course
AI Programming
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Electronica-ICT
AI PROGRAMMING
Artificial intelligence is no match for natural stupidity
, AI Programming Elektronica-ICT
H1: Introductie ................................................................................................................. 2
1.1 Inhoud van AI Programming.......................................................................................................... 2
1.2 Toepassingen van AI Programming ............................................................................................... 2
H2: Agents ........................................................................................................................ 2
2.1 Wat is Artificial Intelligence? ......................................................................................................... 2
2.2 Multidisciplinair domein................................................................................................................ 3
2.3 Terminologie.................................................................................................................................. 3
2.4 Agents ............................................................................................................................................ 3
2.5 Performatiemetriek (POAS) ........................................................................................................... 4
2.6 Agent Program............................................................................................................................... 5
H3: Zoekalgoritmen .......................................................................................................... 6
3.1 Introductie ..................................................................................................................................... 6
3.2 Visualisatie..................................................................................................................................... 6
3.3 Criteria voor zoekalgoritmen......................................................................................................... 6
3.4 Uninformed Zoekalgoritmen ......................................................................................................... 7
3.5 Informed Zoekalgoritmen ............................................................................................................. 7
H4: Lokale Zoekalgoritmen................................................................................................ 8
4.1 Introductie ..................................................................................................................................... 8
4.2 Optimalisaties ................................................................................................................................ 8
4.3 Hillclimb Algoritme ........................................................................................................................ 8
4.4 Simulated Annealing...................................................................................................................... 8
H5: Adverserial Zoekalgoritmen ........................................................................................ 8
5.1 Introductie ..................................................................................................................................... 8
5.2 Minimax Search ............................................................................................................................. 9
H6: Naive Bayes ................................................................................................................ 9
6.1 Introductie ..................................................................................................................................... 9
6.2 Kanstheorie ................................................................................................................................... 9
6.3 Naive bayes classifier..................................................................................................................... 9
H7: Machine Learning ..................................................................................................... 10
7.1 Introductie ................................................................................................................................... 10
7.2 Problemen ................................................................................................................................... 11
7.3 Lineaire Regressie ........................................................................................................................ 11
7.4 Gradient Descent ......................................................................................................................... 12
7.5 Nearest Neighbour Classification ................................................................................................ 12
7.6 K-Means Clustering ..................................................................................................................... 13
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