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FIT-SUIAcad. year: 2023/2024
Overview of methods for solving AI tasks, including game playing. Logic and its use in task solving and planning. 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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Number of ECTS credits
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Entry knowledge
Basic knowledge of state space search (BFS, DFS, A*) and solving simple games (MiniMax) is expected, approximately in the extent of the IZU class taught at bachelor programme at FIT
Rules for evaluation and completion of the course
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.
Students will:
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Basic literature
Recommended literature
Elearning
Classification of course in study plans
specialization NSPE , 1 year of study, winter semester, compulsoryspecialization NBIO , 1 year of study, winter semester, compulsoryspecialization NSEN , 1 year of study, winter semester, compulsoryspecialization NVIZ , 1 year of study, winter semester, compulsoryspecialization NGRI , 0 year of study, winter semester, compulsoryspecialization NADE , 1 year of study, winter semester, compulsoryspecialization NISD , 1 year of study, winter semester, compulsoryspecialization NMAT , 0 year of study, winter semester, compulsoryspecialization NSEC , 0 year of study, winter semester, compulsoryspecialization NISY up to 2020/21 , 0 year of study, winter semester, compulsoryspecialization NCPS , 1 year of study, winter semester, compulsoryspecialization NHPC , 0 year of study, winter semester, compulsoryspecialization NNET , 1 year of study, winter semester, compulsoryspecialization NMAL , 1 year of study, winter semester, compulsoryspecialization NVER , 0 year of study, winter semester, compulsoryspecialization NIDE , 1 year of study, winter semester, compulsoryspecialization NEMB , 0 year of study, winter semester, compulsoryspecialization NISY , 0 year of study, winter semester, compulsoryspecialization NEMB up to 2021/22 , 0 year of study, winter semester, compulsory
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The subject includes three homework assignments: