School of Engineering and Digital Sciences
School of Engineering and Digital Sciences
School of Engineering and Digital Sciences
The Master of Science in Computer Science Program is a two-year program (120 ECTS credits) designed to provide students with advanced mastery of the core CS disciplines balanced with exposure to emerging areas (such as mobile and pervasive computing, multi-factor authentication, privacy and security, persistent data collection, “big data” analysis and data mining).
Students will receive the knowledge and experience that demonstrates domain expertise, and the ability to either continue their educational training at the doctoral level or immediately utilize their training in key economic sectors such government, business, industry, health care, and education.
The NU program is distinguished by its stringent and progressive curriculum (more credits than in a typical program, with more courses, and a research thesis requirement), grounded in Computer Science, by its physical resources (labs and equipment), and the quality, experience (professional, technical and international), and the diversity of the program faculty.
The program is further distinguished by the emphasis on hands-on skills development, with a balance between theory and entrepreneurial practice covering both traditional and emerging fields, and by the strong emphasis on the integration of research in teaching and learning.
The program runs exclusively in English, which serves as the “lingua franca” of the field: all major journals publish in English, and all major conferences use English as the primary language of discourse.
The emphasis on communication skills, embedded throughout the curriculum, combined with the multilingual capabilities of the students, will provide the students with competitive advantage as future leaders of IT innovation in Kazakhstan.
Aims and Objectives
The MSc in Computer Science Program is designed to:
1) Prepare graduates to participate effectively in the emerging “knowledge economy”, driven by information technology;
2) Provide the skills and experience for graduates to design and manage technology projects in a collaborative and interdisciplinary manner;
3) Provide CS & IT educators with the context and technical knowledge to train the next generation of Kazakh students;
4) Prepare researchers in CS and related fields to collaborate and compete with international peers in a global market of ideas and innovation.
Total Number of Credits: 120 ECTS
Year 1: Fall Semester (Semester 1)
Year 1: Spring Semester (Semester 2)
Year 2: Fall Semester (Semester 3)
Year 2: Spring Semester (Semester 4)
Program Core Courses
An accelerated course on modern software design and implementation, which includes modules on abstraction and encapsulation techniques, component-based design, advanced data structures, algorithms, and performance.
This course introduces students to representative research methodologies such as surveys, interviews, experimentation, and case studies. Students then review the fundamental approaches to research design, data collection, analysis, and presentation.
The course requires students to select a topic, identify authoritative sources, generate an annotated bibliography, and compose a survey paper representing the current state-of-the-art in the selected topic area, and prepare a presentation based on the survey paper. The course includes a series of research presentations, presented by domain experts, to expose students to current research programs, and facilitate the thesis topic selection by students.
An accelerated course on modern software design and implementation, which includes modules on abstraction and encapsulation techniques, component-based design, advanced data structures, algorithms, and performance.
Student will conduct independent work under the direction of a supervisor on a research problem in the student's designated area of research. The student will prepare and defend the thesis.
The course requires students to select a topic, identify authoritative sources, generate an annotated bibliography, and compose a survey paper representing the current state-of-the-art in the selected topic area, and prepare a presentation based on the survey paper. The course includes a series of research presentations, presented by domain experts, to expose students to current research programs, and facilitate the thesis topic selection by students.
This course introduces the students to best-practice pedagogical methods and innovations in teaching, under the mentorship of senior faculty. The students will conduct classroom and laboratory observations of prominent instructors, using a variety of teaching and learning styles. Later, they will apply educational, instructional, and assessment methodologies in recitation sections of their respective disciplines under the supervision of an experienced faculty member. The will summarize their observations and experiences in a final report.
Students will apply educational and instructional methodologies in laboratory and practical sessions of their respective disciplines under the supervision of an experienced faculty member. They will summarize their observations and experience in a final report.
Program Elective Courses
In the course, we will investigate different models of computation, such as finite-state machines and push-down automata, and discuss their strengths and limitations. We will then look at the historical and theoretical background of Turing machines and computability, and we will investigate ways to demonstrate (un-)decidability and (non-)recognizability of languages. Computational intractability and NP-completeness will also be discussed as part of the course.
The course examines seminar works on the origin of information theory, relating to data generation, storage, and transmission, and related topics of compression, error detection and recovery, and the transformation of data into information, using both structured forms (database, query languages) and unstructured forms (crawling, indexing, data mining).
This course focuses on the use of formal methods in the modelling, specification and analysis of systems. Various kinds of systems from a number of application domains, such as human-computer interaction, software systems, distributed systems, systems biology and ecology, will be considered. Students will learn how to utilize formal notations and automated tools for simulation and analysis.
This course covers the latest advances in probabilistic, statistical and network-based computational models for real world problems such as computer vision and decision making. Topical coverage will begin at hidden Markov Models, through Markov networks, up to conditional random fields. Additionally, we will never cover novel advances in artificial neural networks such as convolutional neural networks, deep learning models and recurrent neural networks.
Recent advances in technology have led to orders-of-magnitude increases in the amount of data that is being produced and archived. This phenomenon has been generically referred to as Big Data. Examples of such data include internet traffic and logs, social media, large scale scientific projects, and health records. The process of deriving actionable insights from these large volume and heterogeneous data sets is referred to as Big Data Analytics, and poses new challenges in all aspects of computing. This course will review essential statistical and machine learning techniques for data processing and examine their scalability. Students will gain an understanding of current best practices in Big Data processing and complete assignments and projects with a variety of relevant software tools.
The course will start with setting the context by exploring the relationships between data mining, artificial intelligence, machine learning, deep learning, and statistics fields. It will then introduce knowledge representation, input preparation (preprocessing), fundamental data mining techniques (like probabilistic modeling, linear modeling, association mining, etc.), minimum description length (MDL) principle, validation techniques, decision trees, association rules, instance-based learning models, data transformations, data projections, Bayesian methods, and ensemble learning techniques (bagging, boosting, stacking, etc.) within the supervised, unsupervised and semi-supervised learning frameworks. Data vis-
ualization and warehousing methods along with examples from various application areas will also be visited to illustrate the use of data mining in decision support systems and processes.
ualization and warehousing methods along with examples from various application areas will also be visited to illustrate the use of data mining in decision support systems and processes.
This course is a one-semester course intended for M.S. students in Computer Science and Data Science graduate programs. It enhances the student’s knowledge in computer vision concepts and approaches such as image formation, camera imaging geometry, feature detection and matching, stereo, image classification, scene understanding, and deep learning. In class, we will develop the intuitions and mathematics of the vision approaches. We will expose students to a number of real-world applications that are important to our daily lives. To further differentiate between theory and practice, we will guide students through a series of research projects such that they will have chance to implement cutting edge computer vision algorithms. Students will also gain experience through research papers, with an emphasis on proper computer vision practices with appropriate applications.
This course is a one-semester course intended for graduate students in Computer Science and Data Science graduate programs. It introduces the students to the concepts and existing models and techniques of deep learning. The main themes of the course are benefits, properties and challenges of deep learning; introduction to machine learning and optimization; challenges and common approaches; regularization; convolutional neural networks; deep recurrent networks and sequence learning; generative adversarial networks; autoencoders; advanced deep learning topics; applications. The students will learn how to design a deep learning architecture. The course presents several common deep learning techniques and exposes the students in learning how to use deep learning in a very efficient manner. Students will gain experience through assignments and projects, with an emphasis on proper deep learning practices with appropriate applications.
This course covers the design of complex hardware-software based systems with an emphasis on embedded devices. Topics cover models for representing hardware and software components of a system, hardware-software partitioning, design space exploration, performance analysis and estimation, scheduling, real-time aspects and hardware interfacing. The course will have practical sessions introducing students to different hardware types such as microcontrollers, microcomputers with real-time operat-
ing systems and mobile devices with high connectivity.
ing systems and mobile devices with high connectivity.
This course focuses on the design, specification, and verification of distributed systems and their underlying algorithms. Topics such as synchronization, mutual exclusion, safety, liveness, and consensus will be covered. Students will learn how to utilize formal notations and automated tools for simulation and verification of distributed programs as part of this course.
The roots of computer science can be traced to modeling things such as trajectories of artillery shells and cryptographic protocols, both of which pushed the development of early computing systems in the early and mid-1940’s. A principal approach to modeling and simulation is abstraction, so that real world systems can be effectively simulated on a machine. In this course, we will examine modeling techniques such as Monte Carlo methods, stochastic processes, queuing theory, and Markov chains. Assessment and evaluation of approaches will also be covered. Important application areas that may be
investigated include computer systems and networks, diagnostics, economics and finance, and urban planning.
investigated include computer systems and networks, diagnostics, economics and finance, and urban planning.
This course is a one-semester course intended for graduate students in Data Science and Computer Science graduate programs. This course focuses on the design, analysis and implementation of database management systems. Most of the topics will be covered as case studies using commercial and open source database components such that the students can learn a wide-range of tools and techniques and also assess the trade-offs. Student will implement their own database management system as the integrated course project. Therefore, strong programming skills are required. The course will cover topics such as relational, document, key/value data models, storage models and architectures, query languages (e.g. SQL), various types of indexing, transaction management and recovery, query processing, distribute/parallel database architectures, and big data and NoSQL database models and systems.
In this course, students will be introduced to the most salient concepts in quantum computing, a relatively recent approach that specifically targets computing on the atomic and subatomic levels. The basics of quantum computing, including quantum states, unitary operators, and measurements will be presented. Through reversible and quantum circuits, students will learn and understand the advantages and limitations of quantum computing. Finally, different models of quantum computing will be given as illustrations of potential technological implementations.
Financial practices have substantially benefited from the domains of computer science (CS) and artificial intelligence (AI), to the point that most trading in major financial markets are now governed by algorithms. This course aims to provide the CS students with the necessary background and guidance in order to have them practically apply their CS and AI knowledge in financial domains. The course consists of 4 parts: In the first part of the course, all necessary financial background will be provided.
In the second part, tangible mechanics of the domain (like the Financial Information Exchange, FIX, protocol, and order book dynamics) will be covered. In the third part, algorithmic side of the financial transactions will be elaborated on. And in the last part, quantitative strategies (like portfolio construction, high-frequency trading, news handling, etc.) which make heavy use of statistics, AI, data mining, time series analysis, and performance evaluations will be covered.
In the second part, tangible mechanics of the domain (like the Financial Information Exchange, FIX, protocol, and order book dynamics) will be covered. In the third part, algorithmic side of the financial transactions will be elaborated on. And in the last part, quantitative strategies (like portfolio construction, high-frequency trading, news handling, etc.) which make heavy use of statistics, AI, data mining, time series analysis, and performance evaluations will be covered.
This course aims at enabling Master’s students to include cognitive aspects in the modeling of interactive systems in order to capture human errors that would be overlooked when verifying the system in isolation or in an ad hoc interaction context. The course starts with an introduction to cognitive science and a presentation of the human capabilities and limitations and how they affect the interaction process. It then illustrates the use of traditional cognitive models as well as new research approaches. Finally, cognitive modelling is integrated in a software engineering context in order to support the verification of interactive systems. Students’ assessment includes practical modeling exercises, a report and presentation on classical and recent research works, and examinations.
Students will learn a number of important topics in the field of computational biomedicine. They will study various topics within the biomedical domain, including primers of anatomy, physiology as well as the basis of various biomedical imaging techniques. Signal processing tools, such as filter theory, artifact rejection, as well as PCA and CSP will be covered. Additionally, we will cover how uni- as well as multi-variate features can be employed for decoding. Practical computing sessions will be carried out and students will also perform a project, where all research-related steps will be covered.
This course, which requires no background in business studies, exposes the students to fundamental ideas regarding innovation and entrepreneurship. Topics covered will include: how to identify business opportunities and to acquire customers, how to develop a business model, understand investments, and manage risks.