PB015 AI Essentials: Field Foundations and Modern Applications

Faculty of Informatics
Spring 2027
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. Mgr. Bc. Vít Nováček, PhD (lecturer)
Guaranteed by
doc. Mgr. Bc. Vít Nováček, PhD
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Prerequisites

No strict prerequisites, but an ability and willingness to grasp introductions and illustrations of key AI techniques is certainly an advantage for smoothly passing the course. 


Note that while these introductions and illustrations will predominantly be intuitive and narrative, describing a selected (small) number of key notions will benefit from going slightly technical; this is where being reasonably comfortable with high school-level math may help.

Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Abstract

For decades, AI was mostly an academic sub-discipline of computer science with practical impact limited to narrow application scenarios the general public barely ever noticed. This, however, has changed rather dramatically in recent years. Owing to the rapid advances in deep learning and large language model technologies, AI is now poised to disrupt the way we humans work, create, even live. Therefore, it is critical for professionals in virtually every field to understand AI at least at the intuitive level, even if they do not ever intend to actually write a single line of AI code. 


This course aims at providing such an intuitive understanding. It will shed some light on what AI is (and what it isn’t), and provide broader historical and scientific context of the modern AI approaches. The course will also review examples of realistic problems, showing where certain AI techniques can help, and where they might best be avoided, helping you to thread this treacherous and dynamic terrain more confidently after passing the course.

Learning outcomes
- The graduates will gain a proper understanding of what AI is, what are its sub-disciplines and what has been their historical evolution up until the latest mega-hype.
- The graduates will learn about the modern AI techniques, and how to put them in a broader context of what has been motivating the AI developments over the years.
- The graduates will gain basic knowledge on how to select, apply and evaluate appropriate AI techniques in a variety of practical tasks across different data modalities.
- The graduates will be able to explain and justify all things AI to a broad range of stakeholders (product owners, sales or PR managers, collaborators, customers, but also elderly relatives panicking about the looming AI apocalypse).
Key topics

Motivations, historical overview (from Lovelace through Turing to Hinton et al.; classification of AI sub-disciplines; course overview)


Block A (AI classics):

  • Search and optimisation 1/2 (state space search)
  • Search and optimisation 2/2 (local search)
  • Knowledge representation (KR) and reasoning (logic, semantic networks, ontologies, rules, knowledge graphs, automated reasoning, dealing with uncertainty)

Block B (Learning systems):

  • Machine learning (motivations, general overview, examples of supervised and unsupervised approaches)
  • Reinforcement learning (basic principles - environments, policies, Markov decision processes, value function; high-level approaches: direct policy search, Monte Carlo methods, gradient-based methods)
  • Artificial neural networks (motivations, general principles, examples of architectures)

Block C (Modern AI):

  • Transformers and Large Language Models (motivations, general principles, examples of architectures, application scenarios)
  • AI Data Modalities: Text and Speech (motivations, general principles, examples of approaches)
  • AI Data Modalities: Tables, Series, Images, Graphs (motivations, general principles, examples of approaches)
  • AI Now and Onwards (agents, multimodal learning, explainable AI, environmental, ethical and cultural concerns, future outlook of the field)

Other than the intro and the three thematic blocks with set lecture topics, there will be up to three town-hall meetings organised in selected lectures during the semester. These meetings will be dedicated to open discussion about the course topics, partly motivated by insights from the homework assignments associated with each thematic block (see below for details on these assignments). There may also be one invited lecture from a seasoned AI practitioner.

Approaches, practices, and methods used in teaching
A weekly lecture.
Method of verifying learning outcomes and course completion requirements

Final written exam as the baseline, compulsory evaluation: a mix of multiple-choice, factual questions and essay-form assignments assessing the competence and creativity of students in using AI (or applicable alternatives) in realistic scenarios. One has to acquire at least 50% of the total points from the final exam to pass the course.


Additional incentive points can be acquired during the semester for mini-essay homework assignments aligned with the main thematic blocks. Each student must obtain a minimum number of incentive points to pass the course; the incentive points count towards the overall exam score and can thus improve the final grade (if one passes the 50% exam point threshold, that is).

Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
The course is taught every week.
Listed among prerequisites of other courses
The course is also listed under the following terms Spring 2026.
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