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artificial intelligence course content

Visit the Learner Help Center. Understand what is AI, its applications and use cases and how it is transforming our lives, Explain terms like Machine Learning, Deep Learning and Neural Networks, Describe several issues and ethical concerns surrounding AI, Articulate advice from experts about learning and starting a career in AI. Usability and lifecycle of a business; 4 Ethical, societal, scientific and sustainability consideration in the creation of a business; 5. At the end of the course, the students will have the following competences: 1 basic understanding of formal logic (propositional and predicate calculus), 2. computing the truth of logic formulae using truth table and refutation trees, 3. The approach of the course is rather practical, with the explanation of theoretical concept followed by their translation into algorithmic terms. The course will illustrate how persuasion is generated and how this is related with stereotypes, with the natural difficulty in revisiting the knowledge acquired in the past, with the theory of negations and with how language modifies our behaviour. Department of Information Engineering and Computer Science. Learn the fundamentals of Artificial Intelligence (AI), and apply them. In other cases (among which many of the real world) improvements can be sought with very effective heuristic techniques despite the absence of demonstrations of optimality. In case other courses offered by the University of Trento are selected, the study plan will need a committee approval. started a new career after completing these courses. The students will have to use the skills acquired to solve various real Language problems, or do field work, using the languages spoken in class as a training field, in order to acquire a concrete understanding of what it means to carry out research in linguistics. The course will adopt a combination of simulation models and lab experiences to consolidate the knowledge transmitted during the theoretical lessons. Access to lectures and assignments depends on your type of enrollment. The course includes lab exercises and seminars on selected topics. This information will help you to have an informed discussion on the costs and benefits of AI, and reassure decision makers about implementing an AI solution. This option lets you see all course materials, submit required assessments, and get a final grade. This course aims at providing an overview of the foundations of AI and of its main disciplines (e.g. The properties of the related big data acquired by satellites, aircrafts and drones. In depth knowledge on: 1. how technology and innovation interact at stakeholder level (competition, alliances, networks, markets); 2. "The goal of this course is to study two of the main paradigms of Bio-Inspired Artificial Intelligence, namely: Evolutionary Computation, inspired by evolutionary biology, and Swarm Intelligence, inspired by collective behaviors of social animals. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. problem solving, knowledge representation and reasoning, planning, uncertain knowledge, learning, perception, ...) in an organic way. In the course, we will provide the basis for the application of AI techniques to product and process innovation. At the same time, the course will present and discuss the main regulatory tools that could be applied to AI at different levels (national regulations, European regulations, international regulations). We will present both classical and recent research findings obtained with a variety of cognitive neuroscience methods (fMRI, EEG, MEG, TMS, behavior, neurological patients).The objective of the course is to provide the students with the following competences:- Knowledge of the main topics in cognitive neuroscience- Knowledge of the main experimental techniques and methods- Ability to define specific experimental design to answer experimental questions- Ability to critically understand experimental data and results- Knowledge of the main open questions in cognitive neuroscience. This course covers the topics of AI Terminology, AI Strategy, Workflow of Machine Learning Projects and Workflow of Data Science Projects. Then, I will show how these techniques can be applied, for instance, for solving complex optimization problems, train data-driven models, generate new contents (video-games, websites, art), find bugs in software, evolve programs, or find innovative solutions in robotics, logistics, and engineering. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. The most popular formalisms for the representation of systems and of their properties will be introduced, and the main verification algorithms will be presented, and illustrated by means of application examples. It will cover some of the most promising directions of recent research. Providing the knowledge and the abilities required for the simulation of multibody and multidomain systems, which can be used for concept evaluation and for the generation of open loop models usable in control design, 3. An active approach is used, with students producing written texts and then correcting them individually and as a group. Ability to read a scientific paper through formal semantics extracting at least the most salient points. You will understand its applications and use cases and how it is transforming our lives. Learn with Google AI. These concepts are further extended to deal with motion pictures.

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