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Discipline ID
bf91b86a-62db-4996-b583-29c1ffe6e71e

COURSE DETAIL

PARALLEL AND CLUSTER COMPUTING
Country
Ireland
Host Institution
University College Dublin
Program(s)
University College Dublin
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
117
UCEAP Course Suffix
UCEAP Official Title
PARALLEL AND CLUSTER COMPUTING
UCEAP Transcript Title
PARALLEL & CLUSTER
UCEAP Quarter Units
4.00
UCEAP Semester Units
2.70
Course Description
Nowadays, parallel architectures are not only used for high performance computing. The advent of multicore processors, which can be found in all modern desktops, laptops, mobile, and embedded devices, has turned parallel architectures into the mainstream architecture for commodity computing. Correspondingly, parallel programming paradigm is becoming the predominant one in the mainstream programming practice. The course introduces parallel programming and covers the following topics: vector and superscalar processors: architecture and programming model, optimizing compilers (dependency analysis and code generation), array libraries (BLAS), and parallel languages (Fortran 90); shared-memory multi-processors and multicore CPUs: architecture and programming models, optimizing compilers, thread libraries (Pthreads), and parallel languages (OpenMP); distributed-memory multi-processors: architecture and programming model, performance models, message-passing libraries (MPI), parallel languages (HPF); and hybrid parallel programming for clusters of mutlicore CPUs with MPI+OpenMP.
Language(s) of Instruction
English
Host Institution Course Number
COMP30250
Host Institution Course Title
PARALLEL AND CLUSTER COMPUTING
Host Institution Campus
UC Dublin
Host Institution Faculty
Host Institution Degree
Host Institution Department
Computer Science

COURSE DETAIL

PARALLEL AND DISTRIBUTED ALGORITHMS
Country
Singapore
Host Institution
National University of Singapore
Program(s)
National University of Singapore
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
148
UCEAP Course Suffix
UCEAP Official Title
PARALLEL AND DISTRIBUTED ALGORITHMS
UCEAP Transcript Title
COMPUTNG ALGORITHMS
UCEAP Quarter Units
6.00
UCEAP Semester Units
4.00
Course Description
This course examines fundamental issues in parallel computing (i.e., shared-memory algorithms) and distributed computing (i.e., message passing algorithms), and the relationships between the two. It covers various classic problems in parallel/distributed computing, how to design algorithms to solve these problems, and how to prove the correctness of the algorithms. It also looks at various impossibility results in parallel/distributed computing, as well as how to develop impossibility proofs for simple problems. The topics include mutual exclusion, semaphores, consistency, wait-free synchronization, logical time, global state, consistent snapshots, message ordering, consensus, fault-tolerance, transactions, and self-stabilization. This is a pure algorithm/theory module and does not involve explicit programming (to avoid overlapping with CS3211, which focuses on programming). However, the students need to construct proofs based on code, and also potentially write code (on paper) to specify protocols.
Language(s) of Instruction
English
Host Institution Course Number
CS4231
Host Institution Course Title
PARALLEL AND DISTRIBUTED ALGORITHMS
Host Institution Campus
Host Institution Faculty
Host Institution Degree
Host Institution Department
Computer Science

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MULTIMODAL INTERACTION
Country
Germany
Host Institution
Technical University Berlin
Program(s)
Technical University Berlin
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
133
UCEAP Course Suffix
UCEAP Official Title
MULTIMODAL INTERACTION
UCEAP Transcript Title
MULTIMODL INTERACTN
UCEAP Quarter Units
5.50
UCEAP Semester Units
3.70
Course Description

This course sets the basics for an understanding of multimodal communication between humans and multimodal interaction between humans and machines. The course begins with clarifying the basic principles of human-human communication and human-machine interaction. The course then describes the processes taking place in humans when perceiving auditory, visual, and tactile signals, as well as how these perceptions are integrated in order to form a multimodal perception. The signals can be generated and received by machines which are able to interact with humans in limited domains. The set-up of such machines is discussed, and limitations as well as potential solutions to overcome these limitations is explained.

Language(s) of Instruction
English
Host Institution Course Number
n/a
Host Institution Course Title
MULTIMODAL INTERACTION
Host Institution Campus
Host Institution Faculty
FAKULTÄT IV ELEKTROTECHNIK UND INFORMATIK
Host Institution Degree
Host Institution Department
Softwaretechnik und Theoretische Informatik

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SPECIAL STUDY INTERNSHIP
Country
United Kingdom - Scotland
Host Institution
UC Center, Edinburgh
Program(s)
Intern: Scotland,University of Edinburgh
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Political Science International Studies Computer Science Communication Business Administration
UCEAP Course Number
187
UCEAP Course Suffix
UCEAP Official Title
SPECIAL STUDY INTERNSHIP
UCEAP Transcript Title
SP STUDY INTERNSHIP
UCEAP Quarter Units
8.00
UCEAP Semester Units
5.30
Course Description

In this internship, students work closely with their host organization for up to 12 hours per week over the course of a semester. Final assessment is comprised of a mentor evaluation, a self-evaluation/reflection, and an organizational report.

Language(s) of Instruction
English
Host Institution Course Number
Host Institution Course Title
Host Institution Campus
Host Institution Faculty
Host Institution Degree
Host Institution Department

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SCIENTIFIC COMPUTING IN PYTHON (LEVEL 2)
Country
United Kingdom - England
Host Institution
University College London
Program(s)
Summer at University College London
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
120
UCEAP Course Suffix
S
UCEAP Official Title
SCIENTIFIC COMPUTING IN PYTHON (LEVEL 2)
UCEAP Transcript Title
SCI COMPUTNG/PYTHON
UCEAP Quarter Units
6.00
UCEAP Semester Units
4.00
Course Description

Python has rapidly become the standard in scientific computing. It is however much more than that, receiving much excitement about the application of Python to finance, medicine, mobile technology, online gaming, film industry. Its appeal continues to grow in both academia and industry. Much of the advances of medical technology has been due to Python. This is a an intensive Python programming course with numerous medical and health-based applications. Due to the transferability of these skills, students also study examples from investment banking and quantitative finance. The course assumes no prior knowledge of the Python programming language. However, an interest in biomedicine/health is essential.

Language(s) of Instruction
English
Host Institution Course Number
ISSU0069
Host Institution Course Title
SCIENTIFIC COMPUTING IN PYTHON (LEVEL 2)
Host Institution Campus
Host Institution Faculty
Host Institution Degree
Host Institution Department
Mathematics

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INTRODUCTION TO DATA SCIENCE AND MACHINE LEARNING
Country
United Kingdom - England
Host Institution
London School of Economics
Program(s)
Summer at London School of Economics
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Statistics Computer Science
UCEAP Course Number
157
UCEAP Course Suffix
S
UCEAP Official Title
INTRODUCTION TO DATA SCIENCE AND MACHINE LEARNING
UCEAP Transcript Title
DATA SCI&MACH LEARN
UCEAP Quarter Units
5.50
UCEAP Semester Units
3.70
Course Description

This course provides an introduction to the quantitative analysis of data, blending classical statistical methods with recent advances in computational, and machine learning. Students cover key topics such as the challenges of analyzing big data using statistical methods, and how machine learning and data science can aid in knowledge generation and improve decision-making. Students also explore quantitative methods of text analysis, including mining social media and other online resources.

Language(s) of Instruction
English
Host Institution Course Number
ME314
Host Institution Course Title
INTRODUCTION TO DATA SCIENCE AND MACHINE LEARNING
Host Institution Campus
Host Institution Faculty
Host Institution Degree
Host Institution Department
Data Science Institute

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OPERATING SYSTEMS
Country
Sweden
Host Institution
Lund University
Program(s)
Lund University
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
145
UCEAP Course Suffix
UCEAP Official Title
OPERATING SYSTEMS
UCEAP Transcript Title
OPERATING SYSTEMS
UCEAP Quarter Units
6.00
UCEAP Semester Units
4.00
Course Description
The course covers the following topics: the design of operating systems; CPU scheduling; memory management; virtual memory; secondary memories and file systems; distributed systems; protection and security. The course covers examples of different kinds of operating systems with a focus on Linux. Students gain practical experience from software development on the operating system level. The course required project work, which varies from year to year.
Language(s) of Instruction
English
Host Institution Course Number
EDAF35
Host Institution Course Title
OPERATING SYSTEMS
Host Institution Campus
Engineering
Host Institution Faculty
Host Institution Degree
Host Institution Department
Engineering- Computer Science

COURSE DETAIL

GRAPHICS
Country
Netherlands
Host Institution
Utrecht University
Program(s)
Utrecht University
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
108
UCEAP Course Suffix
UCEAP Official Title
GRAPHICS
UCEAP Transcript Title
GRAPHICS
UCEAP Quarter Units
6.00
UCEAP Semester Units
4.00
Course Description

Computer graphics deals with the processing of visual images and spatial data by a computer. Lectures focus on the very basics of modeling and rendering, i.e., the mathematical description of three-dimensional scenes and how to create realistic images of such models. Foundations of computer graphics, such as transformations and projection of 3D models, hidden surface removal, triangle rasterization, shading, texture mapping, shadows, and ray tracing, and advanced topics in physically-based global illumination.  A brief review of the mathematical basics needed for computer graphics, including linear algebra and other areas of higher mathematics that are important far beyond the field of graphics is included.

Language(s) of Instruction
English
Host Institution Course Number
INFOGR
Host Institution Course Title
GRAPHICS
Host Institution Campus
Utrecht University
Host Institution Faculty
Faculty of Science
Host Institution Degree
Host Institution Department

COURSE DETAIL

DATA MINING
Country
Ireland
Host Institution
University College Dublin
Program(s)
University College Dublin
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
118
UCEAP Course Suffix
UCEAP Official Title
DATA MINING
UCEAP Transcript Title
DATA MINING
UCEAP Quarter Units
4.00
UCEAP Semester Units
2.70
Course Description
This course presents important concepts of data mining and how these concepts are implemented and used in real-world applications. The course integrates the theory and practice of data mining with many references to real-world problems and cases to illustrate the concepts and the implementation issues throughout the lectures. The first chapter is devoted to a brief introduction to background information needed to understand the material. This is followed by data warehouse topics and how they differ from database concepts. The notion of data mining process is explained and how it relates to the complete KDD process, as it is very important to understand that data mining is not an isolated subject. The course reviews techniques used to implement data mining algorithms. The course then explores some core topics of data mining: classification, clustering, and association rules. Other concepts, such as prediction, regression, and pattern matching, are also covered, but viewed as special cases of the three core topics.
Language(s) of Instruction
English
Host Institution Course Number
COMP40370
Host Institution Course Title
DATA MINING
Host Institution Campus
UC Dublin
Host Institution Faculty
Host Institution Degree
Host Institution Department
Computer Science

COURSE DETAIL

INTRODUCTION TO DATABASES
Country
United Kingdom - Scotland
Host Institution
University of Edinburgh
Program(s)
University of Edinburgh
UCEAP Course Level
Upper Division
UCEAP Subject Area(s)
Computer Science
UCEAP Course Number
126
UCEAP Course Suffix
UCEAP Official Title
INTRODUCTION TO DATABASES
UCEAP Transcript Title
INTRO DATABASES
UCEAP Quarter Units
8.00
UCEAP Semester Units
5.30
Course Description

Data is one of the most important assets of any enterprise and plays a central role in many aspects of everyday life, from healthcare, to education, to commerce. In order to be turned into meaningful information that enables and supports decision making, data must be stored, maintained, processed and analysed. Database management systems are complex software programs that allow their users to perform these tasks in an efficient and reliable way. This course is an introduction to the principles underlying the design and implementation of relational databases and database management systems.

Language(s) of Instruction
English
Host Institution Course Number
INFR10080
Host Institution Course Title
INTRODUCTION TO DATABASES
Host Institution Campus
University of Edinburgh
Host Institution Faculty
Host Institution Degree
Host Institution Department
school of informatics
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