> Courseware > KIV > SU-E > Course Description - Portál ZČU

On these pages, you will find (I hope) all necessary pieces of information you might need to successfully complete the course and (more importantly) to easily introduce yourself to the field of machine learning as a fundamental area of artificial intelligence. The course aims to provide a comprehensive and clear overview of the basic paradigms of learning systems and fundamental machine learning techniques, with a focus on their practical application in the field of artificial intelligence and intelligent software. Emphasis is put in particular on understanding those machine learning techniques that constitute a basis or a fundamental principle of more complex modern methods. Furthermore, partial attention is paid to the areas of decision making, problem solving, and, of course, knowledge representation.

What can you expect from this course?

The lecturer's primary goal is to show ways to understand the fundamental principles underlying today's artificial intelligence. You may be disappointed at times that the methods and procedures presented here are not particularly complicated, but they have been chosen to represent important problems faced by machine intelligence and to allow self-study to progress to more sophisticated methods derived from them. We will also often discuss the nature of the problems being solved—such discussions should show that the essence of the problem often lies in something other than what it seems at first glance...

What should you not expect?

Don't expect to learn miraculous spells and directions about how to turn a computer into a genius thinking entity. We won't even deal with complex modern algorithms, as they are usually very complicated in theory and explaining them would take a lot of time, and at the same time they are usually just highly tuned versions of basic procedures (which we cover in the course).

Prerequisites for successful completion

The most important prerequisite is, of course, a willingness to work hard. This is a purely scientific subject, so it is obviously not enough to memorize the lecture texts—you need to "see through" and understand the essence... Some of the techniques taught are somewhat more mathematically demanding and may therefore require, for example, home study of the relevant parts of applied mathematics. We will rely primarily on knowledge of linear algebra (matrices, vectors, spaces) and statistics (probability), and occasionally we will use mathematical analysis (e.g., derivatives).

The ability to study technical materials in English is a highly desirable skill. Since there is a general lack of technical literature in Czech in the field of artificial intelligence, practically all materials are in English. However, for technicians – especially in the field of computer science and information technology – English is the modern Latin and should be your second nature nowadays...

Concluding remark

In recent years, the vast majority of problems that ended up with a bad grade or worse resulted from the student's leaving the handling of the problem to the last minute. We will do everything that we can to help you if you encounter complications with the course, but please discuss your problems with us soon enough...

We wish you every success in your studies.

Kamil Ekštein and Ondřej Pražák
(your instructors)