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.

Summary of the Theoretical Computer Science course, taught at Maastricht University.

Summary of the Reasoning Techniques course, taught at Maastricht University. 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

Summary of the Graph Theory course, taught at Maastricht University. Contains at least the following topics: graphs cliques tours paths flows walks map graphs

Summary of the Machine Learning course, taught at Maastricht University. 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