About the programme
FACULTY OF ENTREPRENEURSHIP AND MANAGEMENT
60610500 – Artificial Intelligence
Students in this program will master English to a high level. In addition, they may choose either French or German as an elective second language.
| Credits | Total Hours | Classroom Hours | Course Work | Practical Training | Independent Study | Total |
|---|---|---|---|---|---|---|
| Lecture | Practical | |||||
| 240 | 7200 | 3150 | 1398 | 1392 | 240 | 120 |
The professional fields for graduates in Artificial Intelligence encompass solving complex problems related to artificial intelligence and digital technologies at state and non-state enterprises, organizations, and institutions, as well as in government administration bodies; and the full range of professional areas connected with research work at the Academy of Sciences of the Republic of Uzbekistan, its affiliated research institutes, research centers, and research-production associations.
In the 1st year, all subjects specified in the curriculum are taught in Uzbek.
Practical training (internship) is aimed at reinforcing theoretical knowledge in general professional and specialized subjects, integrating it with practical (production) processes, and developing the corresponding practical skills, competencies, and qualifications.
Upon successful completion of the 4-year program, graduates are awarded the qualification of "Engineer in Artificial Intelligence."
1 credit equals 30 academic hours.
The GPA indicator is determined every year in the third week of August.
The block of elective subjects is determined by decision of the Higher Education Institution Council, based on a set of subjects that ensures the flexibility and mobility of students in line with labor market demands and the requirements of employers.
When developing the working curriculum based on the academic plan, it is permitted — while maintaining the weekly workload of students — to change the volume of the subject blocks by up to 5%, to change the volume of subjects within the blocks by up to 10%, and to freely determine the weekly workload in individual semesters while maintaining the overall volume of classroom workload.
The timeline of the Final State Certification also includes the defense of the graduation qualification thesis.
Practical classes for specialized subjects included in the curriculum are conducted at general secondary education institutions.
To ensure the integration of theory and practice, students' practical training (internships) are conducted at general secondary education institutions.
Physical education and sports is organized as an elective (facultative) course, based on the student's voluntary choice of sports sections.
Course work is carried out within the framework of the independent study hours allocated to the relevant subject, on the basis of 1 credit.
Components of the Educational Process
| Component | Number of Weeks | Semester |
|---|---|---|
| Theoretical and practical instruction | 105 | 1–7 |
| Practical training (internship) | 15 | 8 |
| Assessments | 22 | 1–8 |
| Final state certification | 4 | 8 |
| Vacations | 54 | 1–8 |
| Introduction to the credit education system | 4 | 1, 2, 4, 6 |
| Total | 204 |
VACATION PERIODS:
Year 1:
- From the 5th week of December to the 1st week of January
- From the 2nd week of June until September
Year 2:
- From the 5th week of December to the 1st week of January
- From the 1st week of July until September
Year 3:
- From the 5th week of December to the 1st week of January
- From the 1st week of July until September
Year 4:
- From the 5th week of December to the 1st week of January
- From the 1st week of July onward
SUBJECTS
- Modern History of Uzbekistan
- Religious Studies
- Philosophy
- Foreign Language 1, 2
- Calculus
- Physics 1, 2
- Differential Equations
- Linear Algebra
- Programming 1, 2
- Academic Writing
- Databases
- Fundamentals of Cybersecurity
- Data Structures and Algorithms
- Electronics and Circuits
- Discrete Structures
- Computer Networks
- Fundamentals of Artificial Intelligence
- Probability and Statistics
- Individual Project
- Computer Vision
- Neural Networks and Deep Learning
- Robotics
- Introduction to Machine Learning
- Signal Processing
- Natural Language Processing
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