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

TBA

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 4075E

Credits 6

Prerequisites and/or Corequisites: Programing 1, Linear Algebra

Course Description

Digital Image Processing is a fundamental course that explores the techniques and algorithms used to manipulate, enhance, and analyze digital images. This course covers both theoretical foundations and practical applications of image processing in various fields, including computer vision, medical imaging, remote sensing, and multimedia. Students will learn about image representation, enhancement, restoration, compression, segmentation, feature extraction, and recognition.

Course Learning Outcomes

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

• Understand digital image representation, acquisition, and pixel characteristics.
• Apply techniques like histogram equalization, contrast stretching, and filtering for image enhancement.
• Implement restoration methods to recover degraded images from noise and blurring.
• Employ thresholding, edge detection, and feature extraction for image segmentation and analysis.
• Learn lossless and lossy compression techniques for efficient image storage and transmission.
• Evaluate time and space complexity of algorithms and compare them to select optimal solutions.
• Apply image processing to various fields like medical imaging and computer vision.
• Develop problem-solving skills through hands-on projects and practical exercises.

Course Assessments and Grading

Physical training

Course # HUSS 3080

Credits 0

Pre-requisites and Co-requisites: None

Course description

The purpose of physical education is to strengthen health, develop the physical and mental abilities of students. Physical exercises and sports games is the way to a powerful and functional body, clear mind and strong spirit. The course is both practical and theoretical, it covers basic concepts of anatomy and physiology as well as health and safety requirements.  

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
• chose 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.