KAIST: https://cais.kaist.ac.kr/totalOpeningCourse
Eksempel på Forhåndsgodkjenning_MikaelBjerga.pdf
Hver undergrad-credit teller 1,85 sp, Hver grad-credit teller 3,33 sp.
KAIST-fag | sp. | NTNU-fag | Komm / 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. | 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. | OK 2018.02.18 | |
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. | 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. | OK 2018.02.18 | |
| CS409 - Software Projects for Industrial Collaboration | 10 | Kundestyrt prosjekt, EiT (O) | 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. | OK 2018.02.18 | |
| 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. | OK 2018.02.18 | |
| 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. | OK | |
| 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 | 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 | OK 2018.02.18 |
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 |