Intelligent Systems
A.Y. 2018/2019
Learning objectives
Fornire le conoscenze di base sull'intelligenza nelle macchine. Acquisire la capacità di analizzare e modellizzare problemi anche di una certa complessità. Acquisire un metodo di analisi e soluzione dei problemi.
Expected learning outcomes
Undefined
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Milan
Responsible
Lesson period
First semester
ATTENDING STUDENTS
Course syllabus
NON-ATTENDING STUDENTS
Symbolic intelligence: Turing machine and Chinese room experiment. Weak and strong position on AI. Collective intelligence. Fuzzy sets and fuzzy systems.
Statistical learning: statistical distribution. Maximum likelihood and least squares. Variance analysis. Bayesian estimate and comparison with regularization.
Agents learning. Supervised, not supervised and reinforcement learning. Clustering and associated metrixes. K-means and quad-tree decomposition. Hierarchical Clustering. Neural networks and non-linear perceptron. Kohonen maps and competitive leraning. Multi-scale regression. Applications.
Reinforcement learning. Associative and not associative setting. Starionary and non-starionary problems Gready and epsilon greedy policies. Markov models. Value function computation and Bellman equations. Learning with temporal differences and Q-learning. Improvement of temporal span through eligibility trace. Stochastic automata.
Biological intelligence. Neuron. Under-threshold behavior and action potential. Structure of the neuron and of the central nervous system. Language. Cortical areas. Visuo-motor trqansformations. Population code. Genetic algoruitms and evolutionary optimization. Parameters role. Examples.
Statistical learning: statistical distribution. Maximum likelihood and least squares. Variance analysis. Bayesian estimate and comparison with regularization.
Agents learning. Supervised, not supervised and reinforcement learning. Clustering and associated metrixes. K-means and quad-tree decomposition. Hierarchical Clustering. Neural networks and non-linear perceptron. Kohonen maps and competitive leraning. Multi-scale regression. Applications.
Reinforcement learning. Associative and not associative setting. Starionary and non-starionary problems Gready and epsilon greedy policies. Markov models. Value function computation and Bellman equations. Learning with temporal differences and Q-learning. Improvement of temporal span through eligibility trace. Stochastic automata.
Biological intelligence. Neuron. Under-threshold behavior and action potential. Structure of the neuron and of the central nervous system. Language. Cortical areas. Visuo-motor trqansformations. Population code. Genetic algoruitms and evolutionary optimization. Parameters role. Examples.
Course syllabus
Symbolic intelligence: Turing machine and Chinese room experiment. Weak and strong position on AI. Collective intelligence. Fuzzy sets and fuzzy systems.
Statistical learning: statistical distribution. Maximum likelihood and least squares. Variance analysis. Bayesian estimate and comparison with regularization.
Agents learning. Supervised, not supervised and reinforcement learning. Clustering and associated metrixes. K-means and quad-tree decomposition. Hierarchical Clustering. Neural networks and non-linear perceptron. Kohonen maps and competitive leraning. Multi-scale regression. Applications.
Reinforcement learning. Associative and not associative setting. Starionary and non-starionary problems Gready and epsilon greedy policies. Markov models. Value function computation and Bellman equations. Learning with temporal differences and Q-learning. Improvement of temporal span through eligibility trace. Stochastic automata.
Biological intelligence. Neuron. Under-threshold behavior and action potential. Structure of the neuron and of the central nervous system. Language. Cortical areas. Visuo-motor trqansformations. Population code. Genetic algoruitms and evolutionary optimization. Parameters role. Examples.
Statistical learning: statistical distribution. Maximum likelihood and least squares. Variance analysis. Bayesian estimate and comparison with regularization.
Agents learning. Supervised, not supervised and reinforcement learning. Clustering and associated metrixes. K-means and quad-tree decomposition. Hierarchical Clustering. Neural networks and non-linear perceptron. Kohonen maps and competitive leraning. Multi-scale regression. Applications.
Reinforcement learning. Associative and not associative setting. Starionary and non-starionary problems Gready and epsilon greedy policies. Markov models. Value function computation and Bellman equations. Learning with temporal differences and Q-learning. Improvement of temporal span through eligibility trace. Stochastic automata.
Biological intelligence. Neuron. Under-threshold behavior and action potential. Structure of the neuron and of the central nervous system. Language. Cortical areas. Visuo-motor trqansformations. Population code. Genetic algoruitms and evolutionary optimization. Parameters role. Examples.
Professor(s)