History of the Development of Artificial Intelligence
Course description sheet
Basic information
- Field of study
- Social Informatics
- Major
- -
- Organisational unit
- Faculty of Humanities
- Study level
- First-cycle studies
- Form of study
- Full-time studies
- Profile
- Practical
- Didactic cycle
- 2025/2026
- Course code
- HIFSS.I2.17988.25
- Lecture languages
- Polish
- Mandatoriness
- Obligatory
- Block
- Core Modules
- Course related to scientific research
- No
Lecturer
Jakub Gomułka
|
Period
Semester 2
|
Method of verification of the learning outcomes
Exam
Activities and hours
Lectures:
14
Laboratory classes: 14 |
Number of ECTS credits
3
|
Goals
| C1 | Introducing students to the history of the development of the artificial intelligence research program and its milestones |
| C2 | Teaching students to apply selected algorithmic techniques developed within the artificial intelligence research program |
Course's learning outcomes
| Code | Outcomes in terms of | Learning outcomes prescribed to a field of study | Methods of verification |
| Knowledge – Student knows and understands: | |||
| W1 | A student knows the history of the development of the artificial intelligence research program from the 1950s to the 2020s and can list the most important techniques associated with this program | IFS1P_W01 | Examination |
| W2 | A student knows and understands the basics of game theory and general issues related to probabilistic reasoning | IFS1P_W01 | Execution of laboratory classes, Examination |
| Skills – Student can: | |||
| U1 | A student can practically implement selected search algorithms | IFS1P_U05, IFS1P_U10 | Execution of laboratory classes |
| U2 | A student applies algorithmic techniques related to constraint satisfaction problems (CSP) | IFS1P_U05 | Execution of laboratory classes, Examination |
Program content ensuring the achievement of the learning outcomes prescribed to the module
Lectures:
- General outline of the history of the artificial intelligence research program
- Fundamental mathematical issues related to artificial intelligence techniques
- Problem-solving and search algorithms
- Constraint satisfaction problems
- Probabilistic reasoning
- Basics of game theory and decision-making under uncertainty
Laboratory Exercises:
- Implementation of selected search algorithms
- Algorithmic solving of constraint satisfaction problems
- Probabilistic programming
Student workload
| Activity form | Average amount of hours* needed to complete each activity form | |
| Lectures | 14 | |
| Laboratory classes | 14 | |
| Preparation for classes | 30 | |
| Contact hours | 5 | |
| Preparation of project, presentation, essay, report | 15 | |
| Examination or final test/colloquium | 2 | |
| Student workload |
Hours
80
|
|
| Workload involving teacher |
Hours
28
|
|
* hour means 45 minutes
Program content
| No. | Program content | Course's learning outcomes | Activities |
| 1. |
General outline of the history of the artificial intelligence research program |
W1 | Lectures |
| 2. |
Fundamental mathematical issues related to artificial intelligence techniques |
W2 | Lectures |
| 3. |
Problem-solving and search algorithms |
W1, U1 | Lectures |
| 4. |
Constraint satisfaction problems |
W1, U2 | Lectures |
| 5. |
Probabilistic reasoning |
W2 | Lectures |
| 6. |
Basics of game theory and decision-making under uncertainty |
W2 | Lectures |
| 7. |
Implementation of selected search algorithms |
U1 | Laboratory classes |
| 8. |
Algorithmic solving of constraint satisfaction problems |
U2 | Laboratory classes |
| 9. |
Probabilistic programming |
W2 | Laboratory classes |
Extended information/Additional elements
Teaching methods and techniques :
Lecture, Demonstration, Project Based Learning, Problem Based Learning
| Activities | Methods of verification | Credit conditions |
|---|---|---|
| Lectures | Examination | A prerequisite for taking the exam is prior completion of the laboratory exercises. A positive final grade is achieved after obtaining a passing result on the exam, following successful completion of the exercises. A condition for passing the course is correctly answering more than 50% of the questions on the written exam |
| Lab. classes | Execution of laboratory classes | A condition for passing the laboratory is the completion of all assigned laboratory exercises in the form of mini-projects. |
Prerequisites and additional requirements
Basic programming skills in Python
Basic knowledge on the contemporary AI technology
Literature
Obligatory- Stuart Russell, Peter Norvig, Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Tom 1, Helion: Gliwice 2023