Course details

Artificial Intelligence and Machine Learning

SUI Acad. year 2022/2023 Winter semester 5 credits

Current academic year

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.


Course coordinator

Language of instruction



Examination (written)

Time span

  • 26 hrs lectures
  • 13 hrs exercises
  • 13 hrs projects

Assessment points

  • 60 pts final exam (written part)
  • 20 pts mid-term test (written part)
  • 20 pts projects




Subject specific learning outcomes and competences

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

Learning objectives

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.

Why is the course taught

Artificial intelligence (AI) and machine learning (ML) are nowadays spread from expected places (Google, Facebook) to recommendation systems in e-shops, games, search for travel itineraries, camera focusing, and many others. SUI course provides a basic overview of algorithms and applications of AI and ML for all students of the master program at FIT, and thus fulfills the "AI" in its title. After SUI, artificial neural networks and other components of ML and AI system wont be "magical black boxes" anymore. You will know the basis they are build around - for many of you, this information will be sufficient; those interested can go deeper in specialized courses.

Study literature

  • Materiály k přednáškám dostupné v Moodlu
  • 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.

Syllabus of lectures

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

Syllabus of numerical exercises

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.

Syllabus - others, projects and individual work of students

The subject includes three homework assignments:

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

Progress assessment


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

Course inclusion in study plans

Back to top