KAIST: https://cais.kaist.ac.kr/totalOpeningCourse
Eksempel på Forhåndsgodkjenning_MikaelBjerga.pdf
De aller fleste fagene til KAIST relatert til datateknologi kan finnes på
https://cs.kaist.ac.kr/education/graduate
https://cs.kaist.ac.kr/education/undergraduate
https://lang.kaist.ac.kr/pages/view/lang_03
KAIST-fag | ECTS | NTNU-fag | Comment | Merk | OK? |
|---|---|---|---|---|---|
| Conversation 1A & 1B | 7,5 | K-emne (tilsvarende av Japansk 1) | Able to use simple Korean sentences in greeting others
| 2018.03.13 LJ | |
| CS409 - Software Projects for Industrial Collaboration | 10 | Kundestyrt prosjekt eller Eksperter i Team | This course aims to help students internalize project-based competencies that are essentially needed in the software industries. First of all, they get to figure out the fundamentals and philosophies of software engineering through panel discussions with the reading list. Also, they are asked to be organized into teams with mentors from the industry companies, and to conduct their own software project based on the infrastructures and tools that are really used in the field, minimizing the gap between academia and practitioners. | updated 30.08.2018 after conversation with Magnus Myrmo Osberg | 2018.02.18 LJ |
| CS500 - Design and Analysis of Algorithms | 10 | TDT4125 - Algoritmekonstruksjon (O) | This course introduces basic techniques for the design and analysis of computer algorithms, such as divide-and-conquer, the greedy method, and dynamic programming. Students learn to reason algorithmically about problems arising in computer applications, and experience the practical aspects of implementing an abstract algorithm. | Obligatorisk for Algoritmer og HPC. Bygger på Introduction to Algorithms, og er dermed strukturert litt lignende som AlgDat + AlgKon | 2018.02.18 OK 2018.03.13 LJ |
| CS510 - Computer Architecture | 10 | TDT4260 - Datamaskinarkitektur | This goal of this course is to provide the student with an understanding of (i) the architectural aspect of the performance issues, and (ii) investigation of the full spectrum of design alternatives and their trade-offs. | 2018.03.13 LJ | |
CS542 - Internet Systems Technology | 10 | IT2805 - Webteknologi | This course reviews the state-of-the-art of today's Internet system as well as service architectures, describes the challenges facing them, and discusses emerging approaches. In particular, the course covers issues around Internet traffic characterization; protocols; server architectures and performance; mobile and pervasive services and systems, virtualization; content distribution; peer-to-peer architecture, quality of services (QoS); and architectural alternatives for applications and services. The goal of the course is to gain understanding of the current research issues and a vision of the next generation Internet system and service architecture. | approved as V. | 2018.02.18 OK |
| CS543 - Distributed Systems | 10 | TDT4190 - Distribuerte systemer | This course provides theoretical knowledge and hands-on experience with distributed systems' design and implementation. The course will focus on the principles underlying modern distributed systems such as networking, naming, security, distributed sychronization, concurrency, fault tolerance, etc. along with case studies. Emphasis will be on evaluating and critiquing approaches and ideas. (Prerequisite: CS510, CS530) | Har forkunnskapskrav om OS og Datamaskinarkitektur fra KAIST. | 2018.02.18 LJ 2018.03.13 LJ |
| CS552 - Models of Software Systems | 10 | Advanced Software Design - TDT4250 | For long time, computer scientists have investigated the problem of automating software development from a specification to its program. So far the efforts were not fully successful but much of the results can be fruitfully applied to development of small programs and critical small portions of large programs. In this course, we study the important results of such efforts and, for that, we learn how to model software systems with formal description techniques, how to model software systems such that the various properties expected of the software systems are verifiable and how to verify various properties of software systems though the models. | 2018.02.18 OK | |
| CS554 - Designs for Software and Systems | 10 | TDT4240 - Programvarearkitektur | Development of software and systems requires to understand engineering design paradigms and methods for bridging the gap between a problem to be solved and a working system. This course teaches how to understand problems and to design, architect, and evaluate software solutions. | 2018.02.18 OK | |
| CS610 - Parallel Processing | 10 | TDT4200 - Parallelle beregninger (O) | This course discusses both parallel software and parallel architectures. It starts with an overview of the basic foundations such as hardware technology, applications and, computational models. An overview of parallel software and their limitations is provided. Some existing parallel machines and proposed parallel architectures are also covered. | Obligatorisk for Algoritmer og HPC | 2018.02.18 OK 2018.03.13 LJ |
CS665 - Advanced Data Mining | 10 | TDT4300 - Data Warehousing and Data Mining | Mining big data helps us find useful patterns and anomalies which lead to high impact applications including fraud detection, recommendation system, cyber security, etc. This course covers advanced algorithms for mining big data. | OK 2018.02.18 | |
| CS676 - Pattern Recognition | 10 | TTK4205 - Pattern Recognition | Through this course, students are expected to acquire general ideas of pattern recognition and its application. Three fields (character, speech and image processing) will be studied in which pattern recognition techniques can be successfully applied. | 2018.02.18 OK | |
| CS672 - Reinforcement Learning | 10 | TDT4173 - Maskinlæring og case-basert resonnering | This course covers reinforcement learning, which is one of the core research areas in machine learning and artificial intelligence. Reinforcement learning has various applications, such as robot navigation/control, intelligent user interfaces, and network routing. Students will be able to understand the fundamental concepts, and capture the recent research trends. | 2018.02.18 OK | |
| CS676 - Pattern Recognition | 10 | TTK4205 - Pattern Recognition | Through this course, students are expected to acquire general ideas of pattern recognition and its application. Three fields (character, speech and image processing) will be studied in which pattern recognition techniques can be successfully applied. | 2018.03.13 LJ | |
CS550 - Software Engineering | 10 | TDT4242 - Avansert Programvareutvikling | This course covers fundamental concepts required in developing reliable softwares in a cost-effective manner. | 2018.05.22 LJ | |
| CS636 - UX-oriented Platform Design Studio Ⅰ | 10 | Eksperter i Team | This course provides a studio-oriented eduction for designing and prototyping UX-oriented SW platforms. Based on user study and creative concept development method, students will learn to extract system requirements, design a platform, and implement the proposed system. This course will emphasize design and implementation aspects for user-oriented SW systems, in addition to basic theoretical aspects for creative concept. | 2018.05.22 LJ | |
CS448 - Introduction to Information Security
| 5,5 | TTM4135 - Informasjonssikerhet | This class introduces the fundamental understanding on cryptographic primitives to apply for a secure system including classical, symmetric and asymmetric cryptosystems with mathematical background. The students can gain the general knowledge on modern cryptography to execute advanced research in information security. | 2018.05.22 LJ | |
| CS402 - Introduction to Logic for Computer Science | 10 | TDT4125 - Algoritmekonstruksjon | This is a course on logic with emphasis on its use for computer science. We redesign this course from scratch this year. The new course will involve a large amount of mathematics and theoretical computer science, in particular, computational complexity, verification, and programming languages. We assume that students are fluent in reading and proving mathematical theorems, and that they understand basic concepts from computability and complexity course, such as decidability, NP-completeness and reduction. | Krever forkunnskap innenfor beregnbarhets- og kompleksitetsteori. Har altså lignende forkunnskapskrav som Algoritmekonstruksjon og tar for seg lignende materiale innenfor kompleksitetsteori. | |
| CS492 - Special Topics in Computer Science<Parallel Computing> | 10 | TDT4200 - Parallelle beregninger | Parallel programming was once used only for specialized supercomputers or high-end server systems. With the advent of multi-core and GPUs, parallel programs have become ubiquitous from mobile embedded systems to supercomputers. With the growing importance of exploiting parallelism, next-generation computer scientists/engineers must have the basic understanding of the fundamentals of parallel programming as well as many-core hardware architectures. In addition, the advent of machine learning and big data analytics have advanced the custom accelerators. In this course, you will learn both aspects of parallel systems, programming and architecture, together. Topics: Programming for shared memory architectures: pthread and openMP. Programming for throughput-based computing with GPUs: CUDA, FPGA programming for accelerators. Theory: Multi-core architectures, GPU architectures, Accelerators for Machine Learning and Data analytics. | ||
| CS443 - Distributed Algorithms and Systems | 10 | TDT4190 - Distribuerte systemer | The goal of this course is to provide students with theoretical basis of distributed system design and hands-on experience with distributed systems. The course will start with introduction to functional programming, and then proceed to the MapReduce-like cloud computing framework. Then we expose students to distributed algorithms. Students learn how to program massively parallel jobs in a cloud computing environment and build theoretical underpinnings to expand MapReduce experience to a greater diversity of cloud computing applications. (Prerequisite: CS330, CS341) | ||
| EE488 - Special Topics in Electrical Engineering<Parallel Computer Architecture: Parallel Computing Systems for Deep Learning> | 10 | TDT4260 - Datamaskinarkitekturer (evt. EiT ettersom at EiT kun har krav om å være et studierelevant fag for å bli godkjent for utvekslingsstudenter) | In this course, we will learn the notion of locality, parallelism and hierarchy and how these concepts are utilized in designing modern high-performance parallel computer architectures such as CPUs and GP-GPUs. Based on the understanding of these parallel computer architectures, we will explore how these systems can be programmed using OpenMP, pthreads, and CUDA/OpenCL, followed by a discussion on their use-cases in accelerating deep learning applications. Students must have already taken EE312(Computer Architecture)or an equivalent course during their undergraduate studies. A strong C++/scripting programming skills with a familiarity in Unix/Linux/MacOS environment are also expected. | ||
10 Comments
Unknown User (magnumos)
KSE526
Korean1 for graduate international students
CS408
Computer Science Project
KSE526
Analytical Methodologies for Big Data
Store distribuerte datamengder og Big data arkitekturer
This course discusses basic analytical methodologies for big data, which are vital to data scientists. Big data analytics calls for extending existing algorithms so that they can support big data. In this course, the instructor will first teach MapReduce, which is the representative framework of processing big data, and then the methodologies of extending data mining algorithms into MapReduce. The students will also learn how to implement those algorithms using Apache Hadoop. As a result, the students will achieve the basic capabilities needed to design the algorithms of big data analytics.
Textbooks:
Programming Assignments:
Unknown User (magnumos)
Letizia Jaccheri Har du mulighet til å markere dette som ok også? Fra konklusjonen på skypemøtet vi hadde i september
Unknown User (taphan)
KAIST-fag
ECTS
NTNU-fag
Comment
Merk
HSS586 -Korean1 for graduate international students
This course aims to help international students at KAIST learn basic Korean communication skills by teaching them to read, write, speak and listen in Korean. It also covers aspects of Korean culture to aid their understanding of the country.
CS408 - Computer Science Project
Unknown User (jacobot)
Magnus og meg har allerede fått CS408 muntlig godkjent som 10 SP for Kundestyrt Prosjekt (EiT er sikkert også fint)
CS408 er ment som en erstatning for CS409, så CS409 tilbys ikke lenger her på universitet.
Unknown User (taphan)
Takk for tips
Unknown User (jacobot)
Hei,
Jeg har måtte endre fagene mine her på KAIST for vår-semesteret ettersom at fagene jeg fikk godkjent før utvekslingen ikke foreleses lenger. Her er mine nye fag og forslag til tilsvarende NTNU-emner.
TDT4190 - Distribuerte systemer
TDT4260 - Datamaskinarkitekturer
(evt. EiT ettersom at EiT kun har krav om å være et studierelevant fag for å bli godkjent for utvekslingsstudenter)
In this course, we will learn the notion of locality, parallelism and hierarchy and how these concepts are utilized in designing modern high-performance parallel computer architectures such as CPUs and GP-GPUs. Based on the understanding of these parallel computer architectures, we will explore how these systems can be programmed using OpenMP, pthreads, and CUDA/OpenCL, followed by a discussion on their use-cases in accelerating deep learning applications. Students must have already taken EE312(Computer Architecture)or an equivalent course during their undergraduate studies. A strong C++/scripting programming skills with a familiarity in Unix/Linux/MacOS environment are also expected.
Letizia Jaccheri
approved
Unknown User (magnumos)
KAIST-fag
ECTS
NTNU-fag
Comment
Merk
Maskinlæring og case-baser ressonering
eller
Metoder i kunstig intelligens
Course Description: Recent developments in machine learning have dramatically changed the world. Among many machine learning approaches, Deep Learning is the core of the recent success. This course will cover deep learning architectures. The topics covered in this course include:
EiT
Probabilistic programming refers to the idea of developing a programming language for writing and reasoning about probabilistic models from machine learning and statistics. Such a language comes with the implementation of several generic inference algorithms that answer various queries about the models written in the language, such as posterior inference and marginalisation. By providing these algorithms, a probabilistic programming language enables data scientists to focus on designing good models based on their domain knowledge, instead of building effective inference engines for their models, a task that typically requires expertise in machine learning, statistics and systems. Even experts in machine learning and statistics may get benefited from such a probabilistic programming system because using the system they can easily explore highly advanced models.
This course has two goals. The first is to help students to be a good user of an expressive probabilistic programming language. Throughout the course, we will use a particular language, called Anglican, but we will emphasise general principles that apply to a wide range of existing probabilistic programming systems. The second goal is to expose the students to recent exciting results in probabilistic programming, which come from machine learning, statistics, programming languages, and probability theory.
TTK4135 - Optimalisering og regulering
eller
IE303312 - Intelligente systemer
Skal dekke Ingeniøremne annet studieprogram
Faget har ikke et klart likt fag på NTNU. Begynnelsen av faget handler om å diskutere det vitenskapelige paper'et "A Dynamic Theory of Organizational Knowledge Creation" fra 1994 innen Organizational Science. Deretter skal det læres om hvordan hjelpe "human decision-making", som å automatisere menneskelig operasjonsanalyse og optimalisering.
30% av karakter bestemmes av et teamprosjekt og 15% av individuelle innleveringer i tillegg til eksamener.
Unknown User (taphan)
KAIST-fag
ECTS
NTNU-fag
Comment
Merk
CS448 - Introduction to Information Security
This class introduces the fundamental understanding on cryptographic primitives to apply for a secure system including classical, symmetric and asymmetric cryptosystems with mathematical background. The students can gain the general knowledge on modern cryptography to execute advanced research in information security.
Graduate students need to do a project with a presentation in additional.
CS453 - Automated Software Testing
This course is concerned with a broad range of software testing techniques, with a heavy emphasis on automation, tools, and frameworks, as well as the research outputs behind them. The topic will include, but are not limited to: black box testing/combinatorial testing, random testing, concepts of coverage, structural testing, mutation testing, regression testing, testability transformation, automated debugging, etc.
Prerequisite
Unknown User (benedihm)
TDT4165 Programmeringsspråk
This is an undergraduate-level introductory course for artificial intelligence. There have been enormous advances in the field of artificial intelligence over the past few decades, but it is not easy to see what frontiers the current AI is facing and what underlying methods are used to enable these advances. This course aims to provide an overview of traditional/emerging topics and applications in AI, and basic skill sets to understand/implement some of the latest AI algorithms.
Some (tentative) topics that will be covered in this course include:
1. Visual recognition (image classification, object detection, semantic segmentation).
2. Natural language understanding (machine translation, text summarization).
3. Generative models (image/audio/text synthesis).
Another very important apsect of this course is that we will go through concrete technology that can help us while dealing with these issues. For example, instead of just saying that privacy is important, we will also look at techniques that allow you to effetively hide your data. Instead of just saying that a society should be fair, we will look at techniques that test large software systems for fairness.
The course objectives are to achieve:
a) understanding of the Korean Alphabet system,
b) proper performance of basic Korean greetings,
c) knowledge of basic Korean vocabulary,
d) knowledge of basic Korean grammar rules, and
e) understanding of the basics of Korean culture.
A multi-disciplinary introduction to Korean history and culture, this course covers both the premodern and modern periods, tracing issues such as origins of Korea and Korean people, ancient Korea, colonial rule, national division and the Korean War, South Korean development in the post-war period, gender and family relations, and popular culture. In addition to historical texts, the course examines modern Korean life through literature, family life, gender relations, and popular media, in conjunction with political and economic transformations. Asking how and why historical events, periods, or people are represented in the way that they are will allow a critical perspective as we examine the formation of Korean culture and identity. It will also help us
understand Korean culture and identity as discursive and ever-changing
This course is intended to introduce psychological science by providing engaging examples of psychological questions and answers. There will be a great emphasis for the nature of psychology as a science, and themes that characterize psychological science will be biological bases of behavior, principles of learning, perceptual and cognitive processes, personality, development and social behavior.