Computer Science

This course introduces the discipline of Bioinformatics to students from both physical science and life science backgrounds. It introduces key biological concepts including the main types of molecules (DNA, RNA, and protein) as well as the cell biological processes involved in their regulation and function in biological systems. Students learn to work with and analyze biological sequences through biological sequence databases, process automation, algorithms, and tools to allow pairwise and multiple sequence alignment, as well as approaches using high-throughput next-generation sequence data.

This course provides theoretical as well as practical introduction to quantum computation. By the end of the course students understand the basics of quantum mechanics, quantum logic and computation, important quantum-algorithms, and work with actual quantum computers and quantum simulators. Covered topics include a basic introduction to quantum mechanics to understand quantum computation, quantum algorithms, Simon's algorithm, the prime factorization algorithm, Grover's search algorithm, mathematical models of quantum computation, their relationships to each other, and to physical systems, and quantum error correcting codes. The exercise component of the course includes a background section on the need for quantum computing and then addresses the following topics: hardware technologies for quantum computers, quantum logic, computation on a quantum computer, and programming on IBM Q.

This is a research internship course offered by Rothberg International School. The course's availability is subject to the availability of suitable academic supervision. Students work in a preapproved organization or research institute for a minimum of 8 hours a week (not including transportation) for a total of 88 hours throughout the semester. Students complete a mid-semester meeting including a report submitted to the Internship Coordinator, time sheets, a one-page reflection summarizing the experience, and a portfolio/research paper. Students are assessed on their hours, reflection and work description assignment, and their portfolio/research paper.

This course offers an introduction to the design, construction, and efficient use of databases, with a focus on relational databases. Topics include: data models; database modeling using the entity/relationship model; relational model; database design; SQL query language; views; integrity; transaction processing.

This course focuses on the basic methods for processing and analyzing data with deterministic and probabilistic tools. It is a preliminary course for deep learning and convolutional neural networks. The course explains how to digitize signals and data in a computer, how to represent them in different bases, and how to use these representations efficiently for various signal processing tasks. Topics include signal quantization and sampling for bit-allocation, system and data representations including but not limited to the Fourier representation, optimality of the Fourier representation, functional maps, convolutions, compression, dimensionality reduction, principal component analysis, restoration of blurred deterministic or randomly distributed data with or without random noise via filtering. Signals and systems are analyzed in the continuous and discrete settings.

This course provides an introduction to artificial intelligence including its challenges, revolution, and achievements, and covers topics within machine learning and deep learning. Topics in machine learning include principles, supervised learning, unsupervised learning, Bayesian methods, linear regression, logistic regression, K-means, and decision trees. Topics in deep learning include foundations, architectures, and algorithms.