PLIN068 Applied Machine Learning

Faculty of Arts
Spring 2024
Extent and Intensity
2/0/0. 3 credit(s). Type of Completion: k (colloquium).
Taught in person.
Mgr. Marek Grác, Ph.D. (lecturer), Mgr. Dana Hlaváčková, Ph.D. (deputy)
Mgr. Eva Klimentová (lecturer)
Guaranteed by
Mgr. Richard Holaj, Ph.D.
Department of Czech Language – Faculty of Arts
Contact Person: Bc. Silvie Hulewicz, DiS.
Supplier department: Department of Czech Language – Faculty of Arts
Thu 9:00–11:40 S505
NOW ( PLIN069 Applied ML Project )
* Understanding of technical English (B2)
* High-school level math
* Basic knowledge of programming in Python
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 15 student(s).
Current registration and enrolment status: enrolled: 16/15, only registered: 1/15, only registered with preference (fields directly associated with the programme): 0/15
fields of study / plans the course is directly associated with
there are 7 fields of study the course is directly associated with, display
Course objectives
The goal of this subject is to teach students state of the art techniques in the field of ML/AI. Students will:
* understand selected algorithms at high level
* apply them to real problems in the selected field
On the basis of obtained knowledge, students will be able to prepare data, propose solutions and evaluate the quality of the created models.
The subject is not primarily suitable for students who want to master individual algorithms but is especially suitable for those who wish to apply those techniques in fields other than informatics.
Learning outcomes
Student will be able to apply relevant techniques in ML/AI. Student will have the basic knowledge of applying ML/AI in problems where structured data, free text, images or time series forecasting is usable. Student will know how to qualitatively evaluate results using existing metrics.
  • Introduction and history of ML/AI
  • Introduction to models and hypotheseis
  • Logistic regression. Decision tTrees
  • Libraries, frameworks and tools for ML/AI
  • Data preparation and process of the annotation
  • Neural network and deep neural network
  • Recurrent and convolutional neural network
  • Interpretation and explainability of the models and their results
  • GPT-3, chatGPT, Midjourney and other large models for text and images
  • Transformers and attention
Teaching methods
Pre-recorded video, homework, exercises
Assessment methods
Homework evaluation, essay, activity, final examination
Language of instruction
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2021, Spring 2022, Spring 2023, Spring 2025.
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