Maastricht University (UM) • Data Science And Artificial Intelligence
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Vakken Data Science And Artificial Intelligence op de Maastricht University (UM)
Er zijn samenvattingen beschikbaar voor de volgende vakken van Data Science And Artificial Intelligence op Maastricht University (UM)
Nieuwste samenvattingen Maastricht University (UM) • Data Science And Artificial Intelligence
Summaries for all 3 courses of period 2 in data science and artificial intelligence, namely computational and cognitive neuroscience, computer science 2 and calculus. Computational and cognitive neuroscience are summarized in mind maps for different topics, calculus is summarized onto 1/2 pages per topic and computer science 2 is a textbook summarized booklet with extra notes added on the sides.
Mind map summaries of topics in computational and cognitive neuroscience like the nervous system, anatomy of memory, attention and the eye, consciousness, sleep, language, evolution of the brain etc.
A summarized booklet of the Big Java textbook on object-oriented programming, GUI's and exception handling.
One page discrete maths summary plus one page of important venn diagrams
Summaries of calculus topics like sequences and series, double integrals, partial derivatives, differential equations etc.
One page summary of each topic in Introduction to Data Science and AI, such as data mining, logic, agents, search, data modeling, information retrieval etc.
Samenvatting van het vak Theoretical Computer Science, gegeven aan de Universiteit Maastricht.
Samenvatting van het vak Reasoning Techniques, gegeven aan de Universiteit Maastricht. 
Contains at least the following topics: 
reasoning techniques 
tree search algorithms 
search 
game playing 
constraint satisfaction problems 
graphs 
states 
logical agents 
proofs 
first order logic 
inference 
planning
Samenvatting van het vak Graph Theory, gegeven aan de Universiteit van Maastricht. 
 
Contains at least the following topics: 
graphs 
cliques 
tours 
paths 
flows 
walks 
map graphs
Samenvatting van het vak Machine Learning, gegeven aan de Universiteit van Maastricht. 
Contains at least the following topics: 
ai 
regression (logistic and linear) 
bayesian networks 
graphs 
classification tasks/models 
clustering 
decision trees 
confidence intervals 
neural nets 
recommender systems 
support vector machine 
instance based learning (nearest neighbour) 
reinforcement learning