Computer Science - Junior

Course # COMP 3071

Credits 6

Prerequisites and/or Corequisites: Data Structures and Algorithm

Course Description

Artificial Intelligence (AI) is one of the most transformative technologies of our time, shaping fields as diverse as healthcare, education, agriculture, business, and creative industries. This course introduces students to the foundations of AI while connecting them to the latest advances that define today’s AI landscape. Students learn the core principles of intelligent agents, search strategies, knowledge representation, reasoning, and planning, as well as the fundamentals of machine learning, natural language processing, and computer vision. The course also explores modern applications such as generative AI, large language models, and ethical considerations around fairness, bias, and responsible use of AI. By the end of the course, students understand how AI systems are built, and also how they impact society and how to design intelligent solutions for real-world challenges.

Course Learning Outcomes

Upon the completion of this course, students will be able to:

• Explain the foundational concepts of AI, including intelligent agents, search strategies, planning, knowledge representation, and reasoning, and describe their relevance in modern AI systems.
• Implement basic AI algorithms such as search, inference, and simple machine learning methods, and evaluate their performance on real-world datasets.
• Develop small-scale intelligent applications that demonstrate the use of AI techniques in areas such as natural language processing, computer vision, or generative AI.
• Analyze the societal and ethical implications of AI, including fairness, bias, transparency, and responsible use, with a focus on applications in education, governance, and emerging economies.
• Integrate classical AI methods with modern machine learning approaches to design solutions that address practical challenges in diverse domains such as education, agriculture, and digitalisation.

Course Assessments and Grading

Course # DMNS 3031

Credits 6

Prerequisites and/or Corequisites: Calculus-I, Calculus-II

Course Description

This course is an introduction to statistics and probability. It is designed to equip students with understanding of foundations of statistics and probability and focuses on using modern statistical packages in examining relevant applications.  The course is a prerequisite for advanced statistics.

Course Learning Outcomes

Upon the completion of this course, students will be able to:

• Define fundamental concepts in statistics such as population, sample, types of data and variables.
• Identify descriptive statistics from inferential statistics.
• Define the role of descriptive statistics and inferential statistics in quantitative analyses.
• Find measure of central tendency and measures of variability for given data sets.
• Create and interpret appropriate visualizations for different types of data using a statistical package such as R, Python etc.
• Apply counting principles, permutations, and combinations to solve problems involving counting and arrangements.
• Define key terms in probability, such as random experiment and event.
• Apply axioms and rules of probability.
• Apply the principles of conditional probability to real world problem.
• Describe types of random variables, probability distributions and their properties.
• Calculate probabilities and expected values for various types of probability distributions such as Binomial, Poisson and Normal distributions.
• Define jointly distribution random variables.
• Explain the joint behavior of multiple random variables using distribution functions.
• Describe the Law of Large Numbers and Central Limit Theorem and how they explain the behavior of sample means in large samples.
• Define entropy, relative entropy and mutual information and their significance in information theory.
• Calculate entropy to analyze the information content of different probability distributions.

Course Assessments and Grading

Course # COMP 3021

Credits 6

Prerequisites and/or Corequisites: Digital Logic and Design

Course Description

This course focuses on the basic architecture of computer systems including fundamental concepts such as components of the processor, interfacing with memory and I/O devices, organization of peripherals, and machine-level operations. The course presents detailed deliberation on various system design considerations along with associated challenges commonly employed in computer architecture such as pipelining, branch prediction, caching, etc., This course provides the students with an understanding of the various levels of abstraction in computer architecture, with emphasis on instruction set level and register transfer level through practical examples.

Course Learning Outcomes

Upon the completion of this course, students will be able to:

• Describe the key components of the computer system along with their functionalities and limitations
• Explain the internal working of processor underneath the software layer and how decisions made in hardware affect the software/programmer
• Examine Instruction Set Architecture (ISA) designs and associated trade-offs
• Analyze factors effecting CPU performance e.g., pipelining and instruction-level parallelism
• Explain the I/O subsystems and memory modules of the computer
• Evaluate design and optimization decisions across the boundaries of different layers and system components

Course Assessments and Grading

Course # COMP 3042E

Credits 6

Prerequisites and/or Corequisites: Data Structures and Algorithms

Course Description

This course introduces mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, design of algorithms used to solve these problems. The course emphasizes the relationship between algorithms and programming and introduces basic performance measures and analysis techniques for these problems. It also covers the time complexity and space complexity of different algorithms to find the best algorithm having less time and space complexity for different problems.

Course Learning Outcomes

Upon the completion of the course, students will be able to:

• Identify the key characteristics of a problem.
• Analyze the suitability of a specific algorithm design technique for a problem.
• Apply different design techniques to design an algorithm.
• Explain different time analysis techniques and notations of algorithms.
• Analyze the time and space complexity of different algorithms.
• Compare different algorithms to select the best solution for a given problem.

Course Assessments and Grading

Course # COMP 3116E

Credits 6

Prerequisites and/or Corequisites: Data Structures and Algorithms

Course Description

This course introduces mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, design of algorithms used to solve these problems. The course emphasizes the relationship between algorithms and programming and introduces basic performance measures and analysis techniques for these problems. It also covers the time complexity and space complexity of different algorithms to find the best algorithm having less time and space complexity for different problems.

Course Learning Outcomes

Upon the completion of the course, students will be able to:

• Identify the key characteristics of a problem.
• Analyze the suitability of a specific algorithm design technique for a problem.
• Apply different design techniques to design an algorithm.
• Explain different time analysis techniques and notations of algorithms.
• Analyze the time and space complexity of different algorithms.
• Compare different algorithms to select the best solution for a given problem.

Course Assessments and Grading

Course # HUSS 3126E

Credits 6

Prerequisites and/or Corequisites: None

Course Description

The unprecedented changes brought by technological development often referred to as digitalization is shaping society, culture, and human identity in today’s digital world. Humans are becoming hybrids, communities are emerging online, many people live in virtual reality, and the digital world has altered social norms and created new communication and values. This transformation required anthropologists to use relevant approaches to study the current digital phenomena. Thus, the course invites students to explore how digitalization shapes human subjectivity and social relationships between individuals, groups, organizations, and communities. The course introduces students to the new theoretical framework for the study of digital phenomena and provides ground for the study of digital culture which is a controversial point of many current debates on digitalization and its impact on society. Thus, the goal is to use anthropological approaches to the study of historical overview of technological development, digital societies with emerging and changing nature of community, their culture and societies.

Course Learning Outcomes

Upon the completion of the course, students will be able to:

• Explain and apply key concepts and approaches in digital anthropology to understand how digital technologies are embedded in cultural and social life.
• Analyze the social, cultural, and ethical consequences of digital technologies, including social media, artificial intelligence, digital platforms, and emerging forms of digital economic activity.
• Design and conduct small-scale digital ethnographic research using appropriate qualitative methods to investigate online communities, practices, interactions, and forms of digital culture.
• Critically examine how digital technologies reflect, reproduce, or challenge existing relations of power and inequality, with particular attention to gender, class, identity, and different social and cultural contexts.

Course Assessments and Grading

Physical training

Course # HUSS 3081

Credits 0

Prerequisites and/or Corequisites: None

Course description

The purpose of the Physical Education course is to promote students’ health and well-being, support the balanced development of their physical and psychological abilities, and foster sustained motivation to maintain a healthy and active lifestyle.

Physical exercises and sports activities contribute to the development of physical capabilities, improvement of fitness levels, enhancement of mental well-being, and the development of discipline and personal qualities.

The course has both practical and theoretical components and is designed to develop the knowledge and skills necessary to maintain health and physical activity and to participate safely and effectively in physical exercises and sports activities.

Course learning outcomes

Upon completion of the course students will be able to:

• perform a range of physical activities
• understand health and safety requirements for a range of physical activities
• Describe the role and progress of sport in Central Asia
• choose an appropriate physical activities program for their age and gender
• identify tiredness and its symptoms to control the body during athletic exercises
• describe the technique of running for a long and a short distance and jumping
• accomplish running for a short and a long distance and jumping according to all necessary norms
• Describe the rules of a range of sports games
• participate in a range of sports games according to their rules and techniques

Course Assessments and Grading

* The course will be graded with PASS/FAIL.