Computer Science Program

Students are required to select at least 12 credits outside of the School of Engineering.

Calculus II

Magnetism

Engineering

Engineering

(Students need to choose and complete 1 of 3 majors)

Advanced machine learning

Bayesian statistics

Students are required to select at least 12 credits outside of the School of Engineering.

1*

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Engineering

1*

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Engineering

1*

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Internship

11*

Magnetism

Engineering

Engineering

(Students need to choose and complete 1 of 3 majors)

(i) Data Science

(ii) Machine Learning/Artificial Intelligence

(iii) Software system

This course is designed to help students develop and improve their ability to effectively reason, evaluate, and respond to information relevant to a problem. The course goes beyond written composition and oral communication by focusing on the structure of arguments and how to avoid common logical errors. Information is analyzed from sources such as news, public records, films, slides, audio recordings, and other media, and then organized into a well-organized essay.

This course will include readings from a variety of world literature. Students will read, reflect on, analyse, discuss and write about the content of these essays. Due to time constraints and the aim of representing as many countries as possible, readings will include short stories, poems, plays and excerpts from longer works such as novels and philosophical treatises. This course introduces students to literary works from a variety of countries and cultures. It also provides guidance and introductions to note-taking and essay writing.

This is a general history course that focuses on the examination of the ancient civilizations of Mesopotamia and Europe from recorded history to the late Middle Ages. The course outlines how these civilizations interacted and developed in terms of customs, religion, and systems of governance. The main content of the course includes Mesopotamia, Greece, Rome, and Medieval Europe.

This course is designed to provide an overview of world history from the period of exploration of the New World and the American Revolution to the end of the 20th century. Significant changes in history have been driven primarily by commercial, military, and democratic forces. Notable events include the industrial revolution, European imperialism, trade and globalization, world wars, the rise of superpowers such as the Soviet Union, and Asian markets.

This course focuses on differential and integral calculus of a single variable, emphasizing applications in a variety of fields. It provides an important foundation for subsequent courses in mathematics, engineering, and the social sciences. The course aims to cover chapters 2 through 8 of the textbook by James Stewart.

Main topics include: Limits of functions (intuitive understanding and rigorous definition); Continuity; Derivatives of functions and their meanings (rate of change and slope of tangent line); Rules for calculating derivatives (including the Chain Rule and implicit derivatives); Linear approximations; Applications of derivatives (optimization); Integration, antiderivatives; Fundamental theorems of calculus; Definite and indefinite integrals; Integration techniques (method by parts, trigonometric substitution, fractional factorization, etc.); Applications of integration in various fields (physics, engineering, economics, and biology).

This course introduces the concepts and methods of microeconomic analysis, including supply and demand analysis, the theory of firms and individual behavior, competition and monopoly, and welfare economics. The course explains the principles, issues, and policies related to the optimal allocation of resources. Throughout the semester, students will also be introduced to the applications of microeconomics to current economic policy issues.

This course is designed to familiarize students with the basic concepts, principles and processes involved in Macroeconomics - the national and global economy. The course focuses on the performance, structure, behaviour and decision-making processes of the economy as a whole, including regional, national and global economies. It also covers aggregate indicators such as GDP, unemployment rate, national income, price indices and the relationship between different sectors of the economy, in order to better understand how the economy as a whole works.

This course is designed to provide a systematic overview of management theory and practice, including the application of management theories to practical problems in planning, organizing, leading and controlling business operations. Throughout the course, case studies, exercises and discussions of relevant articles and research will help students gain a deeper understanding of the areas discussed. Upon completion of the course, the knowledge, skills and tools acquired from the course are expected to provide a foundation for students to build and improve their personal management skills.

This course is designed to help students identify historical, theoretical, and practical perspectives on leadership and communication, and apply these theories and perspectives to real-world problems. Throughout the course, students will develop leadership, problem-solving, and communication skills through participation in leadership discussions, study of supplementary materials such as videos and case studies, reading of lecture notes, participation in team-building activities, and participation in community service learning projects.

Marxist-Leninist philosophy is one of the three components of Marxism-Leninism. The course content consists of 03 chapters, explaining general issues related to the existence and development of the world in general and the existence and development of human society in particular, equipping learners with a correct worldview, a positive outlook on life, as well as a dialectical and scientific methodology, in order to effectively solve problems arising in practice. The course is also the basis for students to well absorb Political Theory subjects, as well as other scientific subjects.

Based on the course objectives, the content of the Marxist-Leninist political economy program is structured into 6 chapters. It helps students grasp the most basic issues about goods, markets; surplus value in the commodity economy, industrialization, modernization, and integration of Vietnam.

Based on the purpose of the course, the content of the Ho Chi Minh Thought subject is structured into 6 chapters, the content discusses the concept of Ho Chi Minh Thought, its origin, stages of development, objects, research tasks and basic ideological contents of Ho Chi Minh.

The subject of Ho Chi Minh Thought is closely related to the subject of the Revolutionary Path of the Communist Party of Vietnam and the basic principles of Marxism-Leninism. Because the Party's path is the creative application and development of Marxism-Leninism and Ho Chi Minh Thought into the reality of the Vietnamese revolution.

Based on the purpose of the course, the content of the scientific socialism program is structured into 7 chapters. Providing students with scientific theoretical bases to understand and have revolutionary faith in the path of building and developing the country in the current transitional period to socialism in Vietnam.

The course History of the Communist Party of Vietnam basically studies the process of formation and development of the Party and the contents of the Party's guidelines set forth in the process of leading the Vietnamese revolution from 1930 to the present. Therefore, the main content of the subject is to provide students with basic and systematic understanding of the Party's viewpoints, guidelines and policies, especially in the period of renovation.

The subject History of the Communist Party of Vietnam has a close relationship with the subject Basic principles of Marxism-Leninism and the subject Ho Chi Minh Thought. Because the Party's line is the creative application and development of Marxism-Leninism and Ho Chi Minh Thought into the practice of the Vietnamese revolution.

This course provides learners with basic knowledge of Physical Education, as well as knowledge of team formation and general development exercises. Through this, learners will know how to organize, manage a group and be able to compose general development exercises.

The course includes Introduction to Tennis; Rules, methods of organizing competitions and refereeing Tennis; Techniques of moving, holding the racket, hitting the ball forehand, hitting the ball backhand, serving; and Knowing how to apply basic Tennis techniques in the grassroots competition system.

The course includes Introduction to Tennis; Rules, methods of organizing competitions and refereeing Tennis; Techniques of moving, holding the racket, hitting the ball forehand, hitting the ball backhand, serving; and Knowing how to apply basic Tennis techniques in the grassroots competition system.

Content issued with Circular No. 03/2017/TT-BGDĐT dated January 13, 2017 of the Minister of Education and Training on promulgating the national defense and security education program in secondary pedagogical schools, pedagogical colleges and universities.

Topics will include improper integrals, introduction to differential equations, polar coordinates, infinite series and sequences, Taylor polynomials, Fourier series, vectors and vector functions, partial differentiation, Lagrange multipliers, and topics in differential calculus.

This course will cover the knowledge and applications of vectors, vector spaces, systems of linear equations, matrices, determinants, linear transformations, inner products, eigenvalues and eigenvectors.

Mechanics is a branch of physics that deals with the motion of objects. The aim of this course is to introduce undergraduate students (mainly freshmen or sophomores) to classical mechanics and its applications to practical problems in science and technology. Laboratory experiments and group activities are also an important part of this course.

This course focuses on the fundamentals of electricity, magnetism and optics, a branch of elementary physics that follows on from the previous course on Mechanics. The aim of this course is to introduce undergraduates to the fundamentals and applications of electricity, magnetism and optics in science and technology. Laboratory experiments and group activities are also an important part of this course.

This course introduces the principles and practices of computer science and programming, along with their impact and potential to change the world. The course focuses on algorithms, problem solving, and programming techniques using a high-level language (Python), with an emphasis on design techniques such as abstraction, encapsulation, problem analysis, and recursion. Students will design, implement, and test programs. The course also covers object-oriented programming and popular Python libraries. This course is a prerequisite for all other courses in computer science.

This course deals with data analysis: Calculating the probability of events in a well-defined probability space; Modeling the events of random phenomena using discrete/continuous random variables; Calculate sample statistics, such as sample mean and sample variance, from a data set; Approximate the distribution of the sample mean using the central limit theorem; Estimate unknown parameters using point estimates /interval; Test the validity of a statistical hypothesis based on information from a data set; Perform linear regression analysis on a data set.

This course is designed to provide students with a solid and early introduction to numerical methods. Students will have the opportunity to learn how to model engineering problems and apply basic techniques to solve them using the Python programming language. The course will promote project-based learning, encouraging students to work on computational design projects and relate course content to those projects.

Analyze, use, and design data structures and algorithms using an object-oriented language such as Java to solve computational problems. Emphasis on abstraction including interfaces and abstract data types for arrays/lists, trees, sets, tables/maps, and graphs and their algorithms.

An introduction to the practices and principles of discrete mathematics dealing with discrete objects. Discrete Mathematics is necessary to see the mathematical structures in the objects you work with and understand their properties. This ability is important for software engineers, data scientists, security analysts, and finance. Discrete Mathematics topics include Mathematical Logic, Sets, Relations, Number Theory and Cryptography, Induction and Recursion, Counting, Boolean Algebra, and Modeling Computation. Prerequisite for all other courses in computer science.

This course provides you with a basic understanding of how computers work. Starting with basic numbers and data representation, we explore how computers store and manipulate information to perform calculations. This is followed by higher-level system designs including memory and input/output.

This course is a study of algorithm design, algorithm complexity analysis, and problem complexity analysis. Design techniques include brute-force, reduce, divide, and transform-and-conquer, dynamic programming, greedy algorithms, iterative improvement, backtracking, and branch-and-bound. The course is organized around some basic strategies of algorithm design, and algorithm design will be taught on par with analysis. Some more abstract but important topics will also be covered: NP-completeness, approximation algorithms, and lower-bound limits.

This course provides an introductory description of the concepts that underlie operating systems, an essential part of any computer system. In particular, the course covers topics such as process and thread management, CPU scheduling, process synchronization and deadlock handling, memory management, I/O devices and storage management, file systems, and security and protection mechanisms.

This course is designed to provide students with a solid foundation in database systems. Topics include data modeling, database design theory, data definition and manipulation languages (i.e. SQL), indexing techniques, query processing and optimization, and database programming interfaces. In addition to relational and semi-structured databases (i.e. JSON), the course also samples a number of other topics related to data management, distributed storage, and parallel processing. Programming projects are required.

Data mining is the process of discovering novel, interesting, and insightful patterns, as well as descriptive, understandable, and predictive models from large-scale data. The main parts of the course include exploratory data analysis, frequent pattern mining, clustering, and classification. The course covers the fundamentals of these tasks, and it also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and graphs and complex networks. It integrates concepts from related fields such as machine learning and statistics, and is also ideal for a course in data analysis. Most of the prerequisite material is covered in the text, especially linear algebra, probability, and statistics.

Data visualization is the representation of data in a graphical format. It plays an important role in representing both small and large scale data. The main objective of this course is to provide you with the skills to leverage data to reveal valuable insights from data by extracting information, understanding data better and making effective decisions. Various visualization libraries like Matplotlib, Seaborn, Ggplot, Plotly, Folium, etc. are introduced in the course.

This course will provide an overview of the fundamentals of machine learning. Students will learn about the types of problems that can be solved, the building blocks, and the fundamentals of model construction in machine learning. Several key algorithms will be explored. By the end of the course, students will have a working knowledge of several supervised and unsupervised learning algorithms along with an understanding of key concepts such as under- and overfitting, regularization, and cross validation. Students will be able to identify the type of problem they are trying to solve, choose the appropriate algorithm, tune parameters, and validate models.

The course provides the foundation of big data and cloud computing: properties, characteristics, data sources, applications and values of big data. The course will go through the vector model, distributed programming using MapReduce and database systems such as SQL, NoSQL and column-based for big data applications. The course focuses more on hands-on experience with big data storage and processing systems on Hadoop, HDFS, YARN, Spark - Spark Streaming, Spark SQL platforms. The course also introduces public clouds such as AWS, Azure, Cloudera and deployment solutions for big data applications on the cloud. Hands-on exercises are mandatory for this course.

This course introduces the fundamental concepts of Artificial Intelligence. We focus on the fundamental aspects of AI as the study of agents that perceive and act. Students will learn about classical problem-solving strategies such as search and planning, as well as more modern topics such as representation and learning. Programming exercises will be provided to illustrate the theoretical material. By the end of this course, students will have a solid foundation in the fundamental topics of Artificial Intelligence.

The Advanced Machine Learning course continues to provide students with in-depth knowledge of modern machine learning methods, building on the foundational concepts learned in previous courses. The course focuses on three main topics: probabilistic learning methods (emphasizing Bayesian theory and Bayesian networks), ensemble learning methods (including bagging and boosting techniques), and time series data processing. This course plays an important role in preparing students to study and apply advanced machine learning techniques to solve complex real-world problems. The course is closely related to courses such as "Machine Learning," "Digital Signal Processing," and "Data Mining," and provides a foundation for more in-depth studies in the field of artificial intelligence.

This course introduces neural networks and deep learning. Deep learning has gained significant attention in the industry by achieving state-of-the-art results in computer vision and natural language processing. Students will learn the fundamentals and advances of deep learning and modern techniques to build modern models such as CNN, RNN, Autoencoder, VAE, GAN, Deep-Q Learning. Students will use TensorFlow at two levels: Low-level and Keras API to build Deep Learning models and also Pytorch.

This course is an introduction to Bayesian analysis and statistical decision theory, focusing on decision making under uncertainty. Topics covered include: decision problem formulation and quantification of its components, optimal decisions, Bayesian modeling, simulation-based methods for achieving Bayesian inference (including MCMC algorithms), and hierarchical modeling.

Design and construction of reliable, maintainable and useful software systems. Programming models and tools for medium to large projects: revision control, tools, performance analysis, UML, design patterns, GUI & Usability, software engineering, testing and documentation. Programming project (using Java programming language) is mandatory for this course.

The ever-increasing development of information technology, such as Wireless Sensor Networks and the Internet of Things, has resulted in a large amount of data and information being collected every day from the environment and from human interactions with the environment. This huge amount of data needs to be processed and returned within a limited period of time. Software and applications that process data sequentially have become a bottleneck and are unable to meet the increasing demands of users. Distributed systems provide the means to connect and utilize computing and storage resources from geographically dispersed computers to perform computing and data analysis tasks. This course introduces the basic concepts of distributed systems, methods for designing and implementing fault-tolerant and scalable systems.

Computer networks are ubiquitous today and many IT applications depend on them. This module covers the technologies of computer networks, especially the Internet as a real-world example. The module will first introduce the TCP/IP layered network architecture model, the services of different layers, the concept of encapsulation, and the communication protocols for different layers. Next, the module will apply a bottom-up approach covering the details of each layer starting from LAN, switched-LAN, virtual LAN (VLAN), STP, interconnection between networks with routers, IP addressing, routing protocols, TCP, UDP, socket programming, and popular application layer protocols such as DHCP and DNS. Upon completion of this module, students will have the essential foundation to take the industry-specific CCENT/CCNA Routing and Switching certification exam if they are interested.

The plan for this course includes: Basic ideas of probability theory; Conditional probability and conditional expectation; Markov Chains in Discrete Time; Poisson Process; Markov Processes in Continuous Time; Brief Introduction to Brownian Motion.

The Web Application Development course provides both foundational and advanced knowledge to help students build, deploy, and maintain modern web applications. The course covers both the frontend, using HTML, CSS, and JavaScript, and the backend, using Node.js, Express, and integrating with databases like MongoDB. As a core course in the Computer Science program, this course introduces students to popular tools like React, reinforces understanding of web application deployment and security, and ties in closely with foundational courses like Programming Fundamentals and Databases, providing a solid foundation for more advanced courses in software development or systems administration.

The Mobile Application Development course is an in-depth course that equips students with comprehensive knowledge and skills to develop applications on mobile platforms (Android and iOS). The course covers topics such as application architecture, user interface design (UI/UX), data processing, API integration, and application deployment to Google Play or the App Store. It is a foundational course that links to subjects such as Basic Programming and Databases, and prepares students for advanced courses such as Cross-Platform Application Development or Mobile Application Security.

IoT Application Development is an in-depth course that provides the knowledge and skills needed to design and implement IoT applications in areas such as smart homes, smart agriculture, and Industry 4.0. The course equips students with an understanding of IoT system architecture, microcontroller programming (Arduino, ESP32), communication protocols (MQTT, HTTP), sensor and actuator integration, and data analysis on cloud platforms. This is a foundational course to apply knowledge from Computer Networks, Embedded Programming, and prepare for advanced courses such as Machine Learning and IoT Cybersecurity.

The main objectives of this course are to provide students with the computational tools necessary to: Understand the main principles of image processing and computer vision. Become familiar with classical and popular algorithms and understand their mathematical basis. Implement (using Matlab/Python) and test commonly used image analysis algorithms. Develop the ability to read and evaluate literature on computer vision, digital signal processing and analysis.

Natural Language Processing (NLP) is one of the most important technologies of today's information age. Applications of NLP can be seen everywhere: web search, advertising, email, customer service, language translation, medical reporting, etc. This course provides students with the most basic architectures, models, and algorithms used in natural language processing. Through lectures, exercises, and a final project, students will learn the skills needed to design, implement, and understand basic models and algorithms in natural language processing.

The course plays an important role in equipping students with practical skills and fundamental knowledge to manage and implement software projects, from planning and requirements analysis to implementation and maintenance. This is an in-depth course, closely linked to subjects such as Programming, Software Engineering and IT Project Management. The content includes project management techniques, software development methods, teamwork, documentation and practice of the entire software project life cycle through assignments and final projects.

This course provides students with the knowledge and skills needed to build information retrieval systems for text and web data, which play an important role in fields such as Computer Science, Data Science and Natural Language Processing. The course content includes the fundamentals of information retrieval, models such as Boolean, vector spaces and machine learning; text indexing techniques, system evaluation, document clustering, classification and ranking. Special emphasis is placed on practical applications in web search, including data collection, the PageRank algorithm and metadata analysis. This course is closely linked to subjects such as Natural Language Processing, Machine Learning and Databases, providing a foundation for the development of intelligent information systems.

cleansing

This course provides a comprehensive understanding of data preprocessing and cleaning techniques, essential skills for any data scientist or analyst. Students will learn how to identify and resolve common data quality issues, such as missing data, outliers, inconsistencies, and noise. Through hands-on exercises, students will gain hands-on experience applying data preprocessing and cleaning techniques to real-world datasets.

This course helps students practice solving a data science problem to apply the knowledge and skills learned in the subjects in the specialized direction of data science. Students are required to choose 1 (or several) data sets on the Kaggle site to practice.

This course introduces data mining concepts, algorithms, and techniques on various types of data sets, including basic data mining algorithms as well as advanced topics such as text mining, graph/network mining, and recommender systems. The course requires a group project with practical exercises to extract useful knowledge from large data sets, in addition to regular assignments. This is a graduate-level course in Computer Science, but is also a good choice for final-year undergraduate students interested in the field, as well as students from other disciplines who need to understand, develop, and use data mining systems to analyze large volumes of data.

The course "Deep Learning in Natural Language Processing Practice" equips students with in-depth knowledge and practical skills in applying deep learning to the field of natural language processing (NLP). This course plays an important role in bridging the gap between deep learning theory and practical NLP problems, helping students build and deploy advanced NLP systems. The course is closely related to courses on Natural Language Processing, Machine Learning, and Deep Learning. The course content includes text preprocessing techniques, popular deep neural network models in NLP (RNN, LSTM, GRU, Transformer), and their applications in tasks such as sentiment analysis, machine translation, question answering, and text summarization.

The Practical Deep Learning in Computer Vision course provides students with in-depth knowledge and practical skills in applying deep learning to the field of computer vision. The course focuses on building, training, and deploying deep learning models for common computer vision tasks such as image classification, object detection, image segmentation, and image generation. It is a logical continuation of the courses on image processing, computer vision, and machine learning, providing students with a solid foundation for researching and developing advanced computer vision applications. The course content includes the architecture of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), and advanced training techniques.

Pattern Recognition is a specialized course in the field of Artificial Intelligence and Machine Learning. The course provides basic to advanced knowledge of pattern recognition methods, including data preprocessing techniques, feature extraction, classification algorithms and performance evaluation. The course equips students with the ability to analyze, design and implement pattern recognition systems for many practical applications, including image processing, audio processing, natural language processing (NLP) and tabular data. This course is closely related to subjects such as Linear Algebra, Calculus, Statistics, Machine Learning and Data Mining. The course content includes types of data patterns, feature extraction, classification models (Naive Bayes, SVM, LDA, PCA, ANN) and their applications in audio, images, NLP and tabular data.

This course develops the skills and knowledge required to identify and solve security problems in a variety of simple interface models.

Topics covered include: Classical cryptography, Modern secret key cryptography including block ciphers (DES, AES) and stream ciphers (RC4), security properties (authentication, integrity, confidentiality, availability), public key cryptography (knapsacks, RSA, Rabin, Elgamal), digital signatures (RSA, DSS, Elgamal), hashing (birthday paradox, Merkle-Damgard construction), MACS, Key management (PKI, certificates, key establishment/exchange/transport, Diffie-Hellman), Identity protocols, Privacy protection (mix-net), Secret sharing. Applications studied include email security, Epayment, Electronic voting, Fair exchange.

The "Data Science Topics" course aims to equip students with in-depth and practical knowledge of data science techniques and applications. The course is organized in a seminar format, with guest speakers who are experts from businesses and universities. Students are encouraged to conduct independent research, work in groups and complete a final report. The course provides a solid foundation for students to take the next courses or apply the knowledge to their careers.

Reinforcement Learning introduces students to an important area of artificial intelligence that focuses on training agents to make optimal decisions in a given environment. This course equips students with knowledge of popular reinforcement learning algorithms, from basic to advanced, and the ability to apply them to real-world problems. The course is closely related to Machine Learning, Artificial Intelligence, and Advanced Mathematics. The course content includes basic concepts of reinforcement learning, algorithms such as Q-learning, SARSA, Deep Q-Networks, and their applications in various fields.

This course covers two basic topics in functions of several variables: multiple integrals and vector fields. These topics pave the way for future developments in advanced mathematics courses or applications in engineering and probability. The course begins with double and triple integrals of functions of two or three variables. Students will learn to use polar, cylindrical, and spherical coordinates in the calculation of multiple integrals. Fubini's theorem and changes of variables will be introduced. The second part of the course focuses on vector field calculus. The main subjects are line and surface integrals. These are connected to the double and triple integrals in the first part of the course by higher-dimensional versions of the Fundamental Theorems of Calculus that students encountered in Math 101: Green's Theorem, Stokes' Theorem, and the Divergence Theorem. The goal is essentially to cover Chapters 15 and 16 of Stewart.

This course introduces problem analysis and the strategies used to manage them, primarily in the context of computing. The course introduces problem classification and formal and informal problem-solving methods. The important role of methods and method classification in problem-solving strategies is emphasized, and the rationale for comparing and analyzing strategies is explained. Basic tools for strategy analysis are also discussed. The course explores appropriate representations for problem solving.

The course equips students with the skills to: review the literature on specific research topics, identify research gaps, and propose new research ideas to fill those gaps. The course will focus on the application of emerging artificial intelligence (AI) algorithms to the field of cybersecurity, such as self-supervised learning, transfer learning, and transformer models. Students will perform tasks such as: Network anomaly detection, Botnet detection and classification in networks, Malware detection. Students will work with publicly available datasets, implementing existing algorithms presented in recent research papers. With the proposed new research ideas, students will need to implement and conduct experiments to evaluate their performance compared to existing algorithms.

The course provides students with skills such as reviewing the research literature on a particular topic, identifying research gaps, and proposing new research ideas to fill those gaps. The course will focus on developing an asynchronous federated learning platform. Students will work with publicly available datasets, implementing existing algorithms presented in recent research papers. With the proposed new research ideas, students will need to implement and conduct experiments to evaluate their performance compared to existing algorithms.

Students do internships at businesses. They are required to have a supervisor and connect with businesses to evaluate their knowledge, skills, attitudes and level of work completion according to schedule. The content of the work is discussed and agreed upon by the teacher and the business.