Computer Science

This course introduces the digital tools and methods used for research in the Humanities.  The theoretical part of the course focuses on basic concepts that are essential for working with large quantities of humanities data, including corpora and databases, searching techniques, information retrieval, and statistical language models. In the practical part of the course, students learn how to do basic text analysis using the programming language Python.
 

This course provides an introduction to single machine organization, architecture and operation. Upon successful completion of the class, students learn how to demonstrably understand how instructions get executed in a sequential processor; be able to perform arithmetic operations in binary and conversions between number systems; be able to compose and analyze small assembly-language programs; explain and illustrate memory concepts and performance improvement measures.

The Internet of Things (IoT), where a large number of physical objects embedded with computing power and sensors connect to the network for seamless cooperation between the cyber domain and the physical world, is revolutionizing our lives. This course serves as an introduction to the IoT and provide a holistic view of the entire spectrum of the IoT system architecture from the devices to the fog and the cloud computing. The focus is on designing IoT systems that balance both the functional and non-functional (communication bandwidth, security, safety, power) requirements. 

This course introduces the R system as a programming language. Covering standard regression methods and then tackling more advanced methods, the course guides students through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. By the end of the course, students are expected to have gained a mastery of using the software R to perform data analysis.  

Course enrollees are assumed to have basic knowledge of statistics and mathematics and are encouraged to install the R system onto their home computer. 

This course offers an introduction to the principles and foundations of data science.

The course provides general techniques for the design of efficient algorithms and, in parallel, develop appropriate mathematical tools for analyzing their performance. In this, it broadens and deepens the study of algorithms and data structures initiated in INF2. The focus is on algorithms, more than data structures.