Education

Curriculum

Graduate Curriculum

Cross-Listing Courses
Course Number Course name Credits Classification
CSI5100-01 Programming language 3 System

Lecture description

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CSI6104-01 Combinatorial Optimization 3 -

Lecture description

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CSI6532-01 Computer architecture 3 System

Lecture description

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CSI6558-01 Software agent 3 -

Lecture description

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CSI7102-01 Advanced pattern recognition 3 AI

Lecture description

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CSI7587-01 Visual Computing Special Lecture 1 3 AI

Lecture description

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CSI7615-01 Next-generation computer technology special lecture 3 System

Lecture description

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CSI7618-01 Computational Imaging Science 3 -

Lecture description

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CSI8741-01 Database system special lecture 3 System

Lecture description

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CSI6101-01 Data warehouse system 3 System

Lecture description

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CSI6701-01 Special lecture on human-computer interaction 3 -

Lecture description

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CSI8192-01 Computer System Special Lecture 3 System

Lecture description

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CSI8734-01 Computer Structure Special Lecture 3 System

Lecture description

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CSI8782-01 Database system application 3 System

Lecture description

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CSI5201-01 Game planning and development 3 -

Lecture description

This course is an intensive course during the summer vacation, for 8 days from August 10 (Mon) to 14 (Fri) and August 17 (Mon) to 19 (Wed), 2020 (10 am to 6 pm) 5) each for a total of 48 hours. Course registration is required first, but lectures are held during summer vacation. This course covers key aspects of the design and development of video games, including game world and level design using Unity, game UI design, game character design, game engine programming, 2D/3D modeling and rendering, and game physics and animation. . This course is a project-based course in which students learn how to apply the knowledge and skills they have acquired to build video games for a variety of platforms.

CSI6203-01 Virtual reality and problem solving 3 -

Lecture description

Investigate and study various problem solving cases using virtual reality, and based on this, create new ideas for problem solving and implement them. The problems to be solved here include the limitations of the current virtual reality system and the resolution of various social problems through virtual reality. This course is a social problem-solving graduate course supervised by the Institute for Higher Education Innovation and the Graduate School, and aims to derive experimental results that suggest social value. Some of the class results can be used as results of future social innovation activities at the Institute for Higher Education Innovation and the graduate school.

CSI6529-01 Linear Programming for Computer Science Students 3 -

Lecture description

Linear programming (LP) is a powerful tool to address various challenges arising in computer science. This course introduces students to the basic theory of linear programming and related computational techniques with practical significance. Topics covered include basic definitions, LP duality, simplex method, ellipsoid method, and applications in computer science.

CSI6541-01 Database system 3 System

Lecture description

This course deals with query processing and data storage methods in general relational database systems. Details include data storage structure, indexing method, query processing algorithm, transaction management, concurrency control, and recovery.

CSI7106-01 Multi-Core Computing Topics 3 System

Lecture description

The course introduces current multicore programming research topics in the areas of memory consistency and lock-less programming, hardware transactional memory, stream processing, parallel algorithms, parallel programming languages and compilers, run-time systems, and frameworks.

CSI7623-01 Social and knowledge graph analysis 3 -

Lecture description

Analyzing Big Data has gained attention lately, and many of such data are in the form of large graphs. One good example is a social network, where billions of users are interconnected with social activities. In this course, we will discuss how to model large-scale graphs and support efficient analysis and retrieval of such graphs. We will then discuss how such understanding can lead to new data-driven solutions in many domains. Students will also discuss incoming research issues by reading research papers and conducting research projects.

CSI7688-01 Broadband Integrated Network Structure Special Lecture 3 -

Lecture description

The course will cover the advanced details about wireless/wired data communications. Through reading papers and standard specs. related to the course topic, presentation, and taking a couple of exams, having sccessfully completed this course, students will be able to:
-Describe the operation of contemporary wireless/wired networking technologies such as network virtualization, IEEE 802.11. 802.15 and LTE system.
-Describe the operation of mobility management protocols

CSI8103-01 Advanced computational theory 3 -

Lecture description

The theory of computation is to determine what can and cannot be computed, how quickly (time complexity) with how much memory (space complexity) and on which type of computational model. We study several advanced computing theory in recent developments on the related research topics.

EEE7501-01 Neural network 3 AI

Lecture description

This course covers various deep learning models commonly associated with neural networks, e.g., multilayer perceptron, encoder-decoder networks, convolutional neural networks, recurrent neural networks and generative networks. Methods to train and optimize the models and methods to perform effective inference with them will be highlighted. The course, which will be taught through lectures and projects, will cover the underlying theory, the range of applications to which it has been applied, and learning from large data sets.

IIE6100-01 Advanced Intelligent Information Engineering 3 -

Lecture description

The objective of this course is to learn machine learning techniques that automatically discover hidden knowledge from massive data sets. This course provides knowledge discovery algorithms such as linear regression, decision tree learning, artificial neural networks, instance-based learning, support vector machines, ensembles, Bayesian learning. In addition, I cover model section and feature extraction methods. The performance of students will be evaluated with two exams and homeworks.

MEU6013-01 Cardiovascular disease prediction technology based on fluid modeling 3 -

Lecture description

This class is for graduate students to understand the convergence of engineering-medicine and to discuss the basics of engineering judgment on the presence or absence of cardiovascular disease based on this. Together, we aim to help understand fluid mechanics for the development of stents or related medical devices, and to cultivate talented people who can apply convergence based on the discussed engineering/medical knowledge.

Basic lectures on computer simulation, machine learning, physiology, etc. for diagnosis prediction, and experience for various demands in cardiovascular hospitals will be applied in practice. Development of cardiovascular medical devices is based on medical necessity, designing devices that apply engineering knowledge, Convergent knowledge and research methods such as interpretation, production, and animal testing are required. For basic explanations of medical necessity and cardiovascular disease, animal experiments, etc., lecture progress and task guidance are required by medical school faculty, and device design, analysis, production, and testing require lecture progress and task guidance by engineering college professors.

Therefore, not only lectures on engineering basic knowledge but also researchers related to cardiovascular disease are invited to provide a forum for discussion on basic medical knowledge and skills. Classes are held in parallel with weekly lectures and meetings with the convergence project team for project execution. In order for the students to discuss basic knowledge related to cardiovascular disease and ideas for manufacturing medical devices, the professor who participates in this class will attend weekly classes and meetings to provide related lectures and advice.

The topics of the term project to be held during class are as follows.

1. Development of a predictive model for cardiovascular hemodynamics based on computational fluid dynamics and machine learning

The following results are expected for students who have successfully taken the class.

1. Application to cardiovascular medicine technology based on computer design-based fluid mechanics.
2. Optimization and application of medical technology systems in the subject of cardiovascular system.
3. Problem solving ability through problem discovery, application, and mathematical/engineering formulation.
4. Cultivating social responsibility through organic convergence between engineering and medical schools and enhancing communication skills between different fields.
5. Identify the latest research trends in medical technology and engineering technology and develop problem-solving skills.

AAI5004-01 Artificial intelligence accelerator 3 AI System Advanced

Lecture description

This lecture learns the hardware structure and characteristics of various neural network accelerators (NPU) designed for high-performance/low-power neural network inference/learning. First, review how the operation of the Convolutional Layer, a core layer of Convolutional Neural Networks (CNNs) that achieves excellent inference performance in image processing, etc. We learn the hardware structures of NPUs for CNN acceleration based on CNN. In addition, hardware-software for artificial neural network acceleration by learning various hardware optimization techniques to achieve shorter inference time, higher inference throughput, and lower energy consumption by using weight and activation sparsity, one of the characteristics of CNN. Learn Co-Design techniques. After that,hardware structure of NPUs for Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)-based Recurrent Neural Networks (RNNs) that achieve higher inference accuracy in other tasks in consideration of the temporal relationship between data, Furthermore, we learn the latest NPU hardware structures for accelerating embedding and attention utilized by natural language processing and recommendation systems (such as Facebook DLRM).

AAI5005-01 Graph and Network Analysis 3 AI

Lecture description

Graphs and networks are powerful ways to represent data and systems in fields as diverse as engineering, science, social science, and brain science. This course deals with representation, analysis, and machine learning techniques for graph and network data.

CSI6103-01 Search and text analysis 3 -

Lecture description

Understanding and using recent technologies for the retrieval and mining of large-scale textual data.

CSI6106-01 Pattern recognition system 3 AI

Lecture description

This course will examine a number of advanced topics of Pattern Recognition

CSI6202-01 Big data analysis system 3 AI System Advanced

Lecture description

Researches on Big data Systems are motivated by emerging applications involving massive or continuous data sets such as life log, Web click data and sensor data. Big Data in these applications can be described as 3V i.e., Volume, Velocity, Variey). Recently lots of open-source based Big data systems have been introduced for diverse purpose. This coure introduces the advanced issuses of variou Big data systems and theiry applications

CSI6512-01 Algorithm analysis 3 -

Lecture description

We will study the design and analysis of algorithms for problems that appear in many areas of computer science.

CSI6589-01 Advanced computer vision 3 AI

Lecture description

This course is a graduate level introduction to computer vision. Although there is no specific pre-requisite course, I expect the students to have undergraduate level knowledge in linear algebra and probability as well as programming skills in C++ and Matlab. I will cover general areas of computer vision : low-level computer vision, geometry, and recognition. Within these areas, some of the topics that will be covered in the class include : image features, color vision, epipolar geometry, stereo vision, bags of features, and statistical vision. Students will work on several projects including a semester-long term project.

CSI7103-01 Artificial Intelligence Special Lecture 3 AI

Lecture description

- This lecture introduces the technology and application of data embedding expressed by deep learning, which has recently attracted a lot of attention.
- Understand the research trend of deep learning-based embedding technology used in language, graph, image, and multiple data fields, and learn methodologies for key issues.
- Accumulate practical experience by carrying out related term projects.

EEE7331-01 Deep Learning Special 3 AI

Lecture description

"This is an advanced course for deep learning and thus several selected frontier research topics in deep learning will be focused.Topics include (conventional and lightweight) CNNs, Graph Convolutional Networks (GCNs), Generative Adversarial Networks (GANs) and its applications, security problems in deep learning (Adversarial Learning) and various specific machine learning problems in deep learning.

Goals:
1. Understand and learn several frontier topics in deep learning
2. Solving the problems related to the topics discussed in the lectures"

AAI5002-01 Data Science and Reinforcement Learning 3 AI

Lecture description

In this course, a broad topics of data science and reinforcement learning will be taught, including data analytics, decision making, optimization, reinforcement learning, multi-armed bandit, Markov decision process, etc. After the semester, students should know about how to model real-world problems as data science or reinforcement learning problems and solve them.

AAI5003-01 Deep Learning for Natural Language Processing 3 AI

Lecture description

TBD

AAI5106-01 Parallel and distributed processing programming 3 System

Lecture description

Big data poses a substantial amount of challenges to modern computing systems and programming paradigms, and parallel/distributed computing is one of them. In this course, we will go over system level and architectural solutions solutions for rapidly processing big data with parallel systems, especially focusing on graph processing.

AAI5601-01 Statistical analysis 3 -

Lecture description

This course introduces statistical reasoning and analysis techniques that are essential for engineers in a wide range of disciplines.

CSI6107-01 Introduction to Approximation Algorithms 3 -

Lecture description

1) Students will be able to design approximation algorithms using various techniques based on linear programming relaxations and other combinatorial methods.
2) Students will be able to rigorously analyze these approximation algorithms.
3) Students will be able to implement approximation algorithms.

This course introduces students to basic techniques in the design of approximation algorithms. In particular, algorithms based on linear programming relaxations and other combinatorial methods including local search will be covered. This course will also discuss the hardness of approximation.

CSI6510-01 Meet the future 3 -

Lecture description

This course introduces graduate students to the latest computer system fields (computer architecture, operating system, system software and hardware) in a seminar format. The lectures are composed of professors and researchers with the best research performance in Korea, and are conducted in the form of seminars and free discussions.

CSI6531-01 Advanced operating system 3 System

Lecture description

The aim of this course is to learn the practical aspect of UNIX system/kernel programming. In particular, the course will focus on the Linux kernel programming which covers the important data structures, algorithms, and programming techniques used in the kernel.

CSI6535-01 Computer network 3 System

Lecture description

The goal of this course is to introduce wireless internet protocols.
Special focus are given to:
(1) the protocols for wireless mobile networks (Cellular, WiFi)
(2) the protocols for wireless IoT(Internet of Things) networks

Selected papers will be used for the lecture (no textbook)
On-line lecture: Recorded lecture video + real-time zoom lecture

CSI6557-01 Evolutionary operation 3 -

Lecture description

This course introduces a set of algorithms in the evolutionary computation that is a computational model inspired by biological evolution, and studies the recent developments by investigating the state-of-the-art research works. Term-project gives the students an opportunite to foster the capability to apply the algorithms to solve real problems.

CSI7105-01 Special Lecture on Calculation Theory 3 -

Lecture description

The theory of computation is to determine what can and cannot be computed, how quickly (time complexity) with how much memory (space complexity) and on which type of computational model. We study the recent developments on the topic and examine their properties and limits.

CSI7619-01 Deep Learning-based Computer Vision Special 3 AI

Lecture description

This is an advanced graduate seminar studying current research literature on trends and topics in deep learning, primarily applied to computer vision. Topics include state-of-the-art neural architectures and training techniques, recurrent models, neural generative models (adversarial networks and variational autoencoders), deep reinforcement learning, self-supervised learning, etc.

CSI8783-01 Advanced Broadband Integrated Network Structure Special Lecture 3 -

Lecture description

The course will cover the advanced details about wireless/wired data communications.
Through reading research papers. related to the course topic, presentation, and taking a couple of exams, having sccessfully completed this course, students will be able to:
-Describe the operation of contemporary wireless/wired networking technologies such as network virtualization, IEEE 802.11. 802.15 and LTE system.
-Describe the operation of mobility management protocols

AAI5007-01-00 Generative adversarial network 3 AI

Lecture description

In computer vision, generative models synthesize images from given inputs, while discriminative models compress images into semantic values. Generative adversarial networks (GANs) are combinations of the two, usually focusing on the generated images. Recent GANs synthesize remarkably realistic images leading to interesting applications: image-to-image translation and image editing. This course covers broad topics in the GAN literature. Some of the papers are listed in the weekly plan.

CSI6201-01-00 Data mining 3 -

Lecture description

Lectures on how to design and implement the process of exploring useful knowledge from the vast amount of operational data produced by the active application of computer and database technology. Students learn various data mining techniques such as association rules, frequent items, sequential patterns, and clustering.

CSI6502-01-00 Computational theory 3 -

Lecture description

The theory of computation is to determine what can and cannot be computed, how quickly (time complexity) with how much memory (space complexity) and on which type of computational model. We study automata theory, computability theory and computational complexity theory in this course.

CSI6545-01-00 Computational Interaction Design 3 -

Lecture description

- Students who wish to understand ""Computational Interaction"", an emerging research methodology for human-computer interaction (HCI)
- Students curious about cutting-edge research in computational interaction
- Students who are already conducting human-computer interaction research and want to view their research from a new perspective
- Students who have a strong engineering background and want to challenge human-computer interaction research in the future

CSI6574-01-00 Semantic Web Service 3 -

Lecture description

This course introduces the concepts, core theories and techniques of knowledge graph and semantic web. Learn how to express, explore, and integrate domain knowledge using the latest technologies related to the Semantic Web.

CSI6702-01-00 Computer Vision Using Deep Learning 3 AI

Lecture description

This course will provide basic and advanced computer vision technologies using deep learning. Topics covered will include: linear classifiers; multi-layer neural networks; convolutional neural networks and their applications to computer vision tasks like object classification and detection; recurrent neural networks and state-of-the-art sequence models like transformers; generative models (generative adversarial networks and variational autoencoders); and deep reinforcement learning. Coursework will consist of programming assignments in Python (primarily PyTorch).

CSI8102-01-00 Knowledge-based system application 3 -

Lecture description

Knowledge-based system technology learning to interpret log information of daily life obtained by various sensors and providing services and practical skills acquisition through actual term-project

- Knowledge-based system technology learning
- Knowledge expression, acquisition, management, and sharing understanding
- Data Understanding Mining, Probabilistic Modeling, Deep Learning
- Log information acquisition, pre-processing, analysis, modeling, visualization, service development

CSI8105-01-00 Advanced compiler design 3 System

Lecture description

Please note: this is the graduate compiler course. It does not share any content with the undergraduate course CSI4104-01 on ``Compiler Design’’. Broadly speaking, the undergraduate course discusses programming language design patterns (for lexing, parsing, semantic analysis, code generation and language run-times), while the graduate course is dedicated to compiler optimizations.

The learning objectives of this course are:
* To understand the techniques used in building optimizing compilers that effectively exploit modern processor architectures.
* To gain hands-on experience in implementing optimizations within a modern research compiler.
* To investigate new ideas in optimizing compilers through a term research project.

The course class-time consists of >= 50% lectures. A large part of the lectures will be conducted early on in the semester, to prepare for assignments and the term research project. Further class-time will be used for in-class discussions on assignments and the term research project, and for discussing selected research papers.

CSI8765-01-00 Defensible Computing Special Lecture 3 System

Lecture description

Computers constituting embedded systems and desktop systems are being used in more and more fields, and especially, since they are used in a space that connects the cyber world and the human world, defensive computing characteristics such as safety and reliability are required. Analyze and study various latest technology trends for such a defensive computer system. In particular, it analyzes the latest problems such as soft errors, shares research contents, and submits technical reports as end-of-semester assignments.

MED9092-01-00 Introduction to artificial intelligence and use of medical data 3 AI

Lecture description

Conducting individual research under the guidance of an advisor

CSI6202-01 Big data analysis system 3 AI System Advanced

Lecture description

Researches on Big data Systems are motivated by emerging applications involving massive or continuous data sets such as life log, Web click data and sensor data. Big Data in these applications can be described as 3V i.e., Volume, Velocity, Variey). Recently lots of open-source based Big data systems have been introduced for diverse purpose. This coure introduces the advanced issuses of variou Big data systems and theiry applications

CSI8760-01 Visual Recognition Special Lecture 3 -

Lecture description

This course will examine a number of advanced topics of Pattern Recognition

CSI8104-01 Semantic Web Service Special Lecture 3 -

Lecture description

The course consists of lecture and paper presentation. The lecture related with the topics may be given in advance of the paper presentation. At the paper presentation, we discuss the state-of-the-art on a set of topics in the current knowledge graph (KG) research. Each week, 2~3 papers on KG related topics will be presented and discussed (see Course calendar below). A choice of papers may be given for the students to pick which one to present. Students are required to read the papers scheduled for presentation each week, to be able to participate in discussions in class.

In detail:
Students must have their slides ready before their presentation. All students must read relevant papers and be prepared for the discussions in class.
Every student has to ask at least one question each class. Class attendance and participation are mandatory. (본 수업에서는 지식 그래프의 생성 및 활용에 관한 최신 논문을 읽고 발표함. 지정 논문과 본인이 선택한 주제의 논문을 1~2주마다 발표할 예정임)

CSI6102-01 Wireless Network Technology Special Lecture 3 -

Lecture description

This is a graduate-level introductory course on mobile wireless networking, focusing on current wireless Internet technologies. At first, we will go over the basic issues of wireless communication. Then, we will discuss the currnet wireless networking technologies of Wireless LAN, 3G/4G/5G Cellular, and IoT networks. This course is not tailored for the students who seek in-depth knowledge on the PHY layer.

CSI6536-01 Network protocol and interpretation 3 -

Lecture description

In this course, we learn the characteristics of various network structures and services that support Internet users. In order to support QoS (Quality of Service) of newly emerged networks and user services such as Internet of Things (IoT), it aims to interpret existing network protocols and algorithms, and to acquire in-depth knowledge about the structure of the latest network protocols. .

CSI6505-01 Multicore programming basics 3 System

Lecture description

Multicore architectures have become ubiquitous in all areas of computing, including embedded systems, desktops, servers, and the Cloud. Because individual processors are not becoming faster anymore, the only way to improve application performance is to harness the computing power provided by multiple cores.

Unfortunately very few engineers know yet how to program multicore systems. The ability to harness the raw computing power provided by massively parallel systems will be a key qualification for tomorrow’s software engineers, and a key technology for IT enterprises.

This course will look at the challenges and techniques involved in programming multicore systems. The course starts out with a brief history of computing to motivate the shift to multicore architectures. Parallelism, execution indeterminism, thread-and-lock-based programming, non-blocking synchronization, and HW acceleration with GPGPUs are introduced in a step-by-step approach that is accompanied by individual programming assignments. The impact of hardware architectures on programmability and performance is highlighted. Emerging trends such as Stream-parallel programming and hardware transactional memory are introduced.

At the end of the course, students will have a knowledge of the fundamental design philosophies that multicore architectures address, software engineering design principles for parallel programs, and multicore programming techniques and emerging best practices.

CSI6205-01 Processor microarchitecture 3 System

Lecture description

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CSI6207-01 Memory-centric system architecture for AI 3 AI System Advanced

Lecture description

While processor cores are at the computation, there are other parts of the system that can easily become a performance and energy bottleneck. In this course, we will cover memory system, interconnection network, and the efforts that are being made to those in the era of AI.

We will first study how DRAM works, and how it serves for the purpose of main memory. We will talk about the scaling challenges posed by the modern technology trend that requires more capacity, bandwidth, energy efficiency, and reliability. Then some of the recent advancements along with the surge of AI will be covered, mainly on processing-in-memory and ML-based memory controllers.

Then we will study interconnection networks, which is at the heart of multi (or many) core systems. The topics will include the topology, routing, flow control and the router microarchitectures. We will study the recent researches on interconnection networks, especially the data-centric network designs for AI and big data processing.

AAI5001 Machine Learning and Pattern Recognition 3 AI

Lecture description

This course aims at providing an overview of concepts, techniques, and algorithms in machine learning that can analyze and model a large volume of data. Topics include regression, pattern recognition, neural networks, statistical learning, clustering, kernel methods, Bayesian learning, etc. The course will also discuss real-world applications of machine learning.

AAI5002 Data Science and Reinforcement Learning 3 AI

Lecture description

In this course, a broad topics of data science and reinforcement learning will be taught, including data analytics, decision making, optimization, reinforcement learning, multi-armed bandit, Markov decision process, etc. After the semester, students should know about how to model real-world problems as data science or reinforcement learning problems and solve them.

AAI5003 Deep Learning for Natural Language Processing 3 AI

Lecture description

TBD

AAI6002 Artificial intelligence method 3 AI

Lecture description

Team teaching on legal issues related to artificial intelligence from the perspective of various legal fields, providing basic legal knowledge necessary for the development and practical use of artificial intelligence

CSI6303-01 Cloud Systems and Machine Learning 3 AI System Advanced

Lecture description

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CSI6724-01 HW/SW Platform Optimization for Machine Learning 3 AI System Advanced

Lecture description

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CSI6202-01 Big Data Analysis Systems 3 AI System Advanced

Lecture description

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TBD Artificial Intelligence System Special Lecture 3 AI System Advanced
(Scheduled to Open 2024-2)

Lecture description

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TBD Artificial Intelligence System Security 3 AI System Advanced
(Scheduled to Open 2025-1)

Lecture description

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TBD On-device Artificial Intelligence Systmem and Optimization 3 AI System Advanced
(Scheduled to Open 2025-2)

Lecture description

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TBD Artificial Intelligence Compiler 3 AI System Advanced
(Scheduled to Open 2026-1)

Lecture description

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TBD Parallel and Distributed Artificial Intelligence System 3 AI System Advanced
(Scheduled to Open 2026-2)

Lecture description

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TBD Neural Processing Unit 3 AI System Advanced
(Scheduled to Open 2026-2)

Lecture description

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TBD Quantization and Neural Architecture Search 3 AI System Advanced
(Scheduled to Open 2027-1)

Lecture description

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TBD Dependable Artificial Intelligence System 3 AI System Advanced
(Scheduled to Open 2027-1)

Lecture description

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