Computer Science

This course provides the basic tools and knowledge needed to design optical systems. At the end of the course, students will be able to take system requirements, select possible components and approaches, create candidate designs, and analyze and optimize their performance. Students learn and utilize standard optical design tools, particularly ray-tracing, as well as learning how to create custom system models with wave, polarization, or Gaussian-beam optical modeling. The course objectives include basic design techniques for ray optics; wave optics in isotopic media; design concepts for optical instruments (microscope, telescope, camera lenses); aberration in optical system (real world problems); how to select optical components (lenses, fibers, optical source and detectors); and optical CAD tools discussion (ZEMAX education version).

This course examines the importance of GIS to building smart cities and the ways in which the technology can be integrated with other ICT in order to support different aspects in urban development. It covers an introduction to smart city and its components; geospatial open data and common spatial data infrastructure; enabling technologies for smart city; delivering smart cities through a geospatial strategy; GIS basics; working with ArcGIS online; using web GIS and geospatial cloud in smart city applications delivery; using 3D GIS in smart city planning and development; using mobile GIS in smart city data collection and public engagement; handling real-time geospatial data for smart city parameters monitoring; applying spatial analytics to solve spatial problems and predictive analysis in smart city planning.

This course provides an overview of modern vision techniques used in man and machine. It focuses on both conceptual understanding of the models and methods as well as practical experience. The course covers state-of-the-art methods for image analysis including how to solve visual processing tasks such as object recognition and content-based image search and retrieval. It provides the necessary mathematical background to understand vision and image processing methods through programming exercises, which include converting a theoretical algorithmic description into a concrete program implementation, comparing computer vision and image analysis algorithms, and assessing their ability to solve a specific task. The course involves a mix of lectures and exercises.

The course consists of two parts, each of fundamental importance for any serious approach to Computer Science: logic and discrete structures. Logic plays a very important role in computer architecture (logic gates), software engineering (specification and verification), programming languages (semantics, logic programming), databases (relational algebra and SQL the standard computer language for accessing and manipulating databases), artificial intelligence (automatic theorem proving), algorithms (complexity and expressiveness), and theory of computation (general notions of computability). Computer scientists use discrete mathematics to think about their subject and to communicate their ideas independently of particular computers and programs. In the course, students consider propositional logic as well as predicate calculus. Students treat propositional logic and predicate calculus as formal systems. Students learn how to produce and annotate formal proofs. As application students briefly consider the programming language Prolog. 

Databases encompass many areas of computer science, from formal logic to programming languages, from operating systems to algorithms and data structures. This course covers in detail the main language for relational databases, SQL. It also cover the theoretical query languages on which SQL's core is based, namely relational algebra and relational calculus. Other important topics covered during the course include normal forms, transaction processing, concurrency control, incomplete data and rudiments of query optimization. Topics include the relational model and rudiments of SQL; query languages: relational algebra and calculus; multisets, grouping and aggregation; database design: constraints and normal forms; advanced SQL: nested queries, triggers, null values, transaction management: concurrent schedules, conflict-serializability, locking; database access from applications: using SQL in a host programming language; and basics of indexing, query evaluation and optimization. 

In today's information-driven economy, firms increasingly rely on data pertaining to markets, products, and consumer behavior to inform strategic decision-making in areas such as pricing, advertising, and customer targeting. When correctly used, these data serve as critical inputs for developing effective marketing strategies. This course equips students with the analytical tools and methodological frameworks necessary to leverage such data for strategic marketing applications. The emphasis is on secondary data, i.e., data generated from actual consumer behavior or firm-level decisions. Examples include aggregate market-level data (e.g., car sales statistics), disaggregate panel data (e.g., household grocery purchases), and individual-level digital traces (e.g., online clickstream data). In contrast, primary data, which are collected through surveys or conjoint studies specifically for a particular research purpose, are covered in the Marketing Research class. Prerequisites: background knowledge on statistics, economics, and econometrics, as well as data analysis and relevant coding skills.