Course detail
Artificial Intelligence and Machine Learning
FIT-SUIAcad. year: 2021/2022
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.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Students will:
- get familliar with basic nomenclature of machine learning, esp. of modern neural networks
- understand the relation between a task, a model and the process of learning
- review classical search-based methods of artificial intelligence and will see the possibilities of combining them with machine learning
- get familliar with basic machine learning models (gaussian models, gaussian classifiers, linear regression, logistic regression)
- get familiar with modern neural networks for solving different tasks (classification, regression, tasks in reinforcement learning scenarios) on various kinds of data (unstructured, image, text, audio) and with methods of their training
Prerequisites
Co-requisites
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.
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Ertel, W.: Introduction to Artificial Intelligence, Springer, second edition 2017, ISSN 1863-7310
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016.
Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., third edition 2010, ISBN 0-13-604259-7
Classification of course in study plans
- Programme MITAI Master's
specialization NADE , 1 year of study, winter semester, compulsory
specialization NBIO , 1 year of study, winter semester, compulsory
specialization NCPS , 1 year of study, winter semester, compulsory
specialization NEMB , 0 year of study, winter semester, compulsory
specialization NGRI , 0 year of study, winter semester, compulsory
specialization NHPC , 0 year of study, winter semester, compulsory
specialization NIDE , 1 year of study, winter semester, compulsory
specialization NISD , 1 year of study, winter semester, compulsory
specialization NMAL , 1 year of study, winter semester, compulsory
specialization NMAT , 0 year of study, winter semester, compulsory
specialization NNET , 1 year of study, winter semester, compulsory
specialization NSEC , 0 year of study, winter semester, compulsory
specialization NSEN , 1 year of study, winter semester, compulsory
specialization NSPE , 1 year of study, winter semester, compulsory
specialization NVER , 0 year of study, winter semester, compulsory
specialization NVIZ , 1 year of study, winter semester, compulsory
specialization NISY up to 2020/21 , 0 year of study, winter semester, compulsory
specialization NISY , 0 year of study, winter semester, compulsory
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Introduction to artificial intelligence, machine learning and their relation
- State space search, game playing
- 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.
- Reinforced learning with NNs and without them
- Knowledge, reasoning, planning
- AI applications 1.
Fundamentals seminar
Teacher / Lecturer
Syllabus
Project
Teacher / Lecturer
Syllabus