Computer Science

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. 

A compiler is a computer program that translates text written in a given language (called the source language) into another language (the target language). With most compilers the source language is a high-level programming language (e.g., C, C++, Java), and the target language is a lower-level representation such as assembly language or byte code. This course focus is on compiler techniques needed to implement programming languages on a virtual machine. The aims are to improve programming skills by learning how a compiler works; to apply the theoretical foundations of compilation techniques; to design and implement a compiler for a small programming language; to learn about virtual machines (the JVM in particular); and to practice software engineering design principles on a medium-sized project. This course covers both practical and theoretical aspects of a compiler. Our main emphasis is on the compiler frontend (i.e., scanning, parsing, semantic analysis) and on code-generation for the JVM. 

This course is an introduction to digital image processing and analysis. Students benefit from an overview of image processing methods (histogram restauration, convolution filters, mathematical morphology, segmentation) and image analysis methods (pattern recognition, identification, etc.). During the course students: learn how to manage 2D, 3D, and animated images; understand human perception and image acquisition; discover image segmentation, registration, and analysis; concrete implementation through existing tools or simple script development; study algorithms to obtain features from images (histogram, filters, descriptors).

Students learn about the foundations, building blocks, and applications of Natural Language Processing (NLP), with an emphasis on approaches based on deep learning. They study the models used to represent words and word meanings. They then use these representations to study classification tasks (e.g. sentiment analysis) and tagging tasks (e.g. part of speech tagging). In addition students view languages as sequences of variable length, from pure language models to machine translation models. Finally students explore approaches that are based on modern neural machine learning algorithms, where linguistic information is provided by instances of uses of language.

This course introduces the subject of artificial intelligence covering the basic principles and technologies of intelligent computer systems and the algorithms to achieve AI and how to develop some AI programs. Topics include: intelligent agents; search techniques for problem solving (uninformed, informed, local, adversarial); knowledge representation; logical inference; propositional logic; reasoning under uncertainty; statistical models and machine learning; probability; Bayes’ nets; and decision theory.