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FIT-SUIAcad. year: 2022/2023
Overview of methods for solving AI tasks, including game playing. Logic and its use in task solving and planning. PROLOG vs. AI. Basic tasks of machine learning, metrics for quality assessment. Basic approaches to ML - decision trees, version spaces, reinforcement learning, active learning. Probabilistic approach to classification and recognition, Gaussian model, its interpretation and training. Linear and logistic regression. Support vector machines. Neural networks (NN) - basic building blocks, principles of training. Practical work with "deep" NNs. Sequential variants of NN. AI applications.
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Learning outcomes of the course unit
Students will:
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Aims
Make students acquainted with the basics of artificial intelligence (AI) and machine learning (ML) that are the basic components of modern scientific methods, industrial systems and end-user applications - for example self-driving cars, cognitive robotics, recommendation systems, recognition of objects in images, chat-bots and many others. Show traditional techniques linked to currently dominating deep neural networks. Introduce basic mathematical formalism of AI and ML, that can be developed in specialized courses. Give an overview of software tools for AI and ML.
Specification of controlled education, way of implementation and compensation for absences
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Basic literature
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eLearning
Classification of course in study plans
specialization NGRI , any year of study, winter semester, compulsoryspecialization NSEC , any year of study, winter semester, compulsoryspecialization NEMB do 2021/22 , any year of study, winter semester, compulsoryspecialization NEMB , any year of study, winter semester, compulsoryspecialization NHPC , any year of study, winter semester, compulsoryspecialization NISY , any year of study, winter semester, compulsoryspecialization NISY do 2020/21 , any year of study, winter semester, compulsoryspecialization NMAT , any year of study, winter semester, compulsoryspecialization NVER , any year of study, winter semester, compulsoryspecialization NADE , 1. year of study, winter semester, compulsoryspecialization NBIO , 1. year of study, winter semester, compulsoryspecialization NNET , 1. year of study, winter semester, compulsoryspecialization NVIZ , 1. year of study, winter semester, compulsoryspecialization NCPS , 1. year of study, winter semester, compulsoryspecialization NISD , 1. year of study, winter semester, compulsoryspecialization NIDE , 1. year of study, winter semester, compulsoryspecialization NMAL , 1. year of study, winter semester, compulsoryspecialization NSEN , 1. year of study, winter semester, compulsoryspecialization NSPE , 1. year of study, winter semester, compulsory
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Fundamentals seminar
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The subject includes three homework assignments: