Computer Science

Are you looking to develop the skills to solve real-world challenges in finance, risk management, and insurance? These fields often deal with unpredictable phenomena—like investment decisions, insurance claim patterns, or pricing derivatives—which require robust stochastic models and advanced machine learning techniques. To tackle these challenges effectively, it’s essential to use robust statistical techniques and calibration methodologies to ensure models are reliable. This course equips students with the tools to apply modern statistical and machine learning methods to these complex problems. Students start by exploring Monte Carlo methods, simulating stochastic processes, and applying Generative Adversarial Networks (GANs) in risk management. They then connect Generalized Linear Models to deep neural networks, discovering their practical applications in the insurance industry. The course also addresses the challenges of calibrating models to ensure their accuracy and reliability. Combining rigorous theory with hands-on coding exercises in Python, students gain experience implementing real-world case studies while strengthening their core data science skills.

This course teaches students to define the phases of a typical compiler, including the front and back end. Students learn to identify tokens of a typical high level programming language define regular expressions for tokens and design implement a lexical analyzer using a typical scanner generator. The course explains the role of a parser in a compiler and relate the yield of a parse tree to a grammar derivation design and implement a parser using a typical parser generator, and how to apply an algorithm for a top down or a bottom up parser construction construct a parser for a small context free grammar. The course describes the role of a semantic analyzer and type checking create a syntax directed definition and an annotated parse tree describe the purpose of a syntax tree. The course focuses on the role of different types of runtime environments and memory organization for implementation of typical programming languages. The course describes the purpose of translating to intermediate code in the compilation process. Students design and implement an intermediate code generator based on given code patterns.

The course provides instruction in agile development methodologies and promotes close collaboration between team members. The development of the project is structured as two product releases as part of the development cycle. Each week, the current state of progress is reported and discussed, both in the lectures and in the group meetings. A project mentor meets with each team weekly and will advise on setting up the team structure including the assignment of roles and responsibilities within the team and on reporting systems both internally and externally. Weekly peer code and design reviews are a core component of the delivery of the course. These are to encourage a team approach to learning and introduce the practicalities of software quality control. 

This course helps master the use of Large Language Models (LLMs) and AI agents for data collection, analysis, and visualization. The curriculum goes beyond traditional data analysis by teaching students how to extract hidden patterns and uncover semantic meanings from dynamically built corpora. This approach enables students to gather insights and design results that can only be achieved with the assistance of AI tools. The course develops an understanding of both the capabilities and limitations of AI tools in research contexts and explores how AI tools can enhance our understanding of social phenomena and examine the strengths and limitations of AI-assisted research. Through hands-on projects, students investigate these questions using real-world data from various sources such as climate negotiations reports, parliamentary speeches, social media discourses, etc. Additionally, the course develop a methodological toolbox by generating an analysis pipeline with the assistance of AI. The course goes over fundamental concepts in Machine Learning and Natural Language Processing and culminates in a student-led investigation project. Working in groups, students develop their own research protocol, collect and analyze data using AI tools, and present their findings in the form of a website. Each student also produces an individual reflective essay on their experience with AI-assisted research.

The course provides an introduction to the essentials of computer game development. The process of development by small independents, even single individuals, shares important features with development by large companies: innovation, creativity, storyboarding, software development, testing, deployment, and (sometimes) marketing. Topics include the economic importance of the computer game industry; common genres of games; the development of game software using specialized tools which promote rapid development through their integration of numerous prepackaged components; techniques for representing objects in 2-dimensional and 3-dimensional space, and determining whether they collide; techniques for equipping non-player characters with AI; techniques for producing special effects; gamification, that is, the provision of enjoyably game-like experience to promote education or customer loyalty or other purposes. 

Topics include regular languages, context-free languages, feature structures, and brief introductions to probabilistic methods in natural language processing and recursive computation of semantic values from grammatical structures.

Topics covered in this course include HTML syntax, composition, and validation; cascading style sheets (CSS); basic dynamic scripting examples; site planning, visual information management, and responsive design; digital image formats; and Search Engine Optimization (SEO). Students provide markup for various HTML elements, attributes, and values associated with the representation of web page content. They use CSS to effectively control the presentation of websites and understand the usefulness of incorporating dynamic scripting into web sites. This course teaches students how to differentiate between alternate image formats that are appropriate for web use, make responsive design layouts using CSS Grid and Flexbox, and understand the importance of consistency, structure, and aesthetics of design and how to achieve these. Students learn current W3C standards and recommendations when planning, designing, and publishing a website.

The goal of this course is to enable students to have an overall understanding of the field of computer networks and a preliminary understanding of related technologies, and to further understand and master the key technologies of the new generation of Internet (including the principles and specific implementation of the technology), and to initially cultivate students' research capabilities in the field of computer networks.

This course examines the rapid development of Information Technology and the relaxation of regulations in the financial industry. Topics include how tech firms can enter the financial industry to reach and provide financial services to customers at scale and the market that is neglected by traditional financial institutions. Under this backdrop, many tech firms build online platforms to mobilize the under-utilized financial resources among customers. This allows customers who need financial services to bypass traditional financial institutions (e.g., banks and venture capital funds) and get served. Students examine peer-to-peer lending platforms, equity-based crowdfunding platforms that link individual investors with founders of startups and how the emergence of these new platforms substantially reduces the financing cost on the borrowers’ side and increases the rate of return on the investors’ side. The course requires students to take prerequisites.

This course requires basic programming skills, which includes an understanding of what variables are; the basic structure of loops; Python indexing, and slicing of iterables. It expands on programming skills from a digital humanities perspective. The course focuses on Python pandas for data analysis and data manipulation and uses matplotlib for visualization. The course also instructs on version control using git. The latter half of the course focuses on social media analysis including network analysis, topic modeling, and sentiment analysis.

It is strongly recommended that students have taken the intermediate course “Python Programming for Digital Humanities” or a similar one before taking this course.