FF:PLIN068 Applied ML - Course Information
PLIN068 Applied Machine Learning
Faculty of ArtsSpring 2025
- Extent and Intensity
- 2/0/0. 3 credit(s). Type of Completion: k (colloquium).
In-person direct teaching - Teacher(s)
- Mgr. Marek Grác, Ph.D. (lecturer), Mgr. Dana Hlaváčková, Ph.D. (deputy)
Mgr. Denisa Šrámková (lecturer)
Mgr. Eva Klimentová (lecturer)
Mgr. Richard Holaj, Ph.D. (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 - Prerequisites
- 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: 0/15, only registered: 0/15, 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.
- Syllabus
- 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
- English
- Further Comments
- The course is taught annually.
The course is taught: every week. - Listed among pre-requisites of other courses
- Enrolment Statistics (Spring 2025, recent)
- Permalink: https://is.muni.cz/course/phil/spring2025/PLIN068