Course detail

Artificial Intelligence and Machine Learning

FIT-SUIAcad. year: 2025/2026

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

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Lukáš Burget, Ph.D.

Department

Department of Computer Graphics and Multimedia (UPGM)

Entry knowledge

Not applicable.

Rules for evaluation and completion of the course

 

  • Half-semestral exam (20pts)  
  • Three homework assignments (20pts) 
  • Semestral exam, 60pts, requirement of min. 20pts.

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:

  • 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

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

C. Bishop: Pattern Recognition and Machine Learning, Springer, 2006
Ertel, W.: Introduction to Artificial Intelligence, Springer, second edition 2017, ISSN 1863-7310
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016.
Materiály k přednáškám dostupné v Moodlu
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 NSEC , 0 year of study, winter semester, compulsory
    specialization NISY up to 2020/21 , 0 year of study, winter semester, compulsory
    specialization NNET , 1 year of study, winter semester, compulsory
    specialization NMAL , 1 year of study, winter semester, compulsory
    specialization NCPS , 1 year of study, winter semester, compulsory
    specialization NHPC , 0 year of study, winter semester, compulsory
    specialization NVER , 0 year of study, winter semester, compulsory
    specialization NIDE , 1 year of study, winter semester, compulsory
    specialization NISY , 0 year of study, winter semester, compulsory
    specialization NEMB , 0 year of study, winter semester, compulsory
    specialization NSPE , 1 year of study, winter semester, compulsory
    specialization NEMB , 0 year of study, winter semester, compulsory
    specialization NBIO , 1 year of study, winter semester, compulsory
    specialization NSEN , 1 year of study, winter semester, compulsory
    specialization NVIZ , 1 year of study, winter semester, compulsory
    specialization NGRI , 0 year of study, winter semester, compulsory
    specialization NADE , 1 year of study, winter semester, compulsory
    specialization NISD , 1 year of study, winter semester, compulsory
    specialization NMAT , 0 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Michal Hradiš, Ph.D.
doc. Ing. Lukáš Burget, Ph.D.
Ing. Karel Beneš

Syllabus

  1. Introduction to artificial intelligence, machine learning and their relation
  2. Basic tasks of machine learning (ML) - detection, classification, regression, prediction, sequence recognition, metrics for quality assessment.  
  3. Basic approaches to ML - decision trees, version spaces, reinforcement learning, active learning. 
  4. Probabilistic approach to classification and recognition - basics of Bayes theory. 
  5. Gaussian model, its interpretation and training, PCA. 
  6. Linear and logistic regression, Support vector machines - basic formulation and kernel trick.  
  7. Neural networks (NN) - basic building blocks, principles of training.
  8. Practical work with deep NNs - mini-batch, normalization, regularization, randomization, data augmentation.  
  9. Sequentional variants of NN: RNN, LSTM, BLSTM, autoencoders, attention models, use of NN embeddings.
  10. Reinforced learning with NNs and without them
  11. State space search, game playing
  12. Local search, constraint satisfaction problems
  13. AI applications. 

Seminar

13 hod., optionally

Teacher / Lecturer

Ing. Michal Hradiš, Ph.D.
Ing. Karel Beneš
doc. Ing. Lukáš Burget, Ph.D.

Syllabus

Lectures will be immediately followed by demonstration exercises (1h weekly) where examples on data and real code in Python will be presented. Code and data of all demonstrations will be made available to the students.

Project

13 hod., optionally

Teacher / Lecturer

Ing. Karel Beneš

Syllabus

The subject includes three homework assignments:

  1. Data modelling and simple classifiers
  2. Construction of a simple neural network
  3. Problem solving by search