Course detail
Artificial Intelligence and Machine Learning
FIT-SUIAcad. year: 2020/2021
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.
Guarantor
Learning outcomes of the course unit
Prerequisites
Co-requisites
Recommended optional programme components
Literature
C. Bishop: Pattern Recognition and Machine Learning, Springer, 2006
Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., third edition 2010, ISBN 0-13-604259-7
Ertel, W.: Introduction to Artificial Intelligence, Springer, second edition 2017, ISSN 1863-7310
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016.
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
- Half-semestral exam (20pts)
- Submission of project (20pts)
- Semestral exam, 60pts, requirement of min. 20pts.
Language of instruction
Work placements
Aims
Classification of course in study plans
- Programme MITAI Master's
specialization NGRI , any year of study, winter semester, 5 credits, compulsory
specialization NSEC , any year of study, winter semester, 5 credits, compulsory
specialization NEMB , any year of study, winter semester, 5 credits, compulsory
specialization NHPC , any year of study, winter semester, 5 credits, compulsory
specialization NISY , any year of study, winter semester, 5 credits, compulsory
specialization NMAT , any year of study, winter semester, 5 credits, compulsory
specialization NVER , any year of study, winter semester, 5 credits, compulsory
specialization NADE , 1. year of study, winter semester, 5 credits, compulsory
specialization NBIO , 1. year of study, winter semester, 5 credits, compulsory
specialization NNET , 1. year of study, winter semester, 5 credits, compulsory
specialization NVIZ , 1. year of study, winter semester, 5 credits, compulsory
specialization NCPS , 1. year of study, winter semester, 5 credits, compulsory
specialization NISD , 1. year of study, winter semester, 5 credits, compulsory
specialization NIDE , 1. year of study, winter semester, 5 credits, compulsory
specialization NMAL , 1. year of study, winter semester, 5 credits, compulsory
specialization NSEN , 1. year of study, winter semester, 5 credits, compulsory
specialization NSPE , 1. year of study, winter semester, 5 credits, compulsory
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Introduction to artificial intelligence, machine learning and their relation
- Basic tasks of machine learning (ML) - detection, classification, regression, prediction, sequence recognition, metrics for quality assessment.
- Basic approaches to ML - decision trees, version spaces, reinforcement learning, active learning.
- Probabilistic approach to classification and recognition - basics of Bayes theory.
- Gaussian model, its interpretation and training, PCA.
- Linear and logistic regression, Support vector machines - basic formulation and kernel trick.
- Neural networks (NN) - basic building blocks, principles of training.
- Practical work with deep NNs - mini-batch, normalization, regularization, randomization, data augmentation.
- Sequentional variants of NN: RNN, LSTM, BLSTM, autoencoders, attention models, use of NN embeddings.
- State space search, game playing
- Knowledge, reasoning, planning
- AI applications 1.
- AI applications 2.
Fundamentals seminar
Teacher / Lecturer
Syllabus
Project
Teacher / Lecturer
Syllabus