Courses

Fall 2022

Information Security
Course goals: learn how to design a secure system, probe systems for weaknesses, write code with fewer security bugs, use crypto libraries correctly, protect (or breach!) privacy, and use your powers ethically. Main topics: basic cryptography, system security, network security, firewalls, malware, web security, privacy technologies, cryptocurrencies, human factors, physical security, economics, and ethics of security.
Instructors: Prateek Mittal
Innovating Across Technology, Business, and Marketplaces
Course teaches engineering students about issues tackled by leading Chief Technology Officers: the technical visionaries and/or managers who innovate at the boundaries of technology and business by understanding both deeply, and who are true partners to the CEO, not just implementers of business goals. Focus will be on thinking like a CTO (of a startup and a large company) from technology and business perspectives, and on software and Internet-based businesses. Industry-leading guest speakers provide perspectives too.
Instructors: Jaswinder Singh
Introduction to Dance Across Cultures
Bharatanatyam, butoh, hip hop, and salsa are some of the dances that will have us travel from temples and courtyards to clubs, streets, and stages around the world. Through studio sessions, readings and viewings, field research, and discussions, this seminar will introduce students to dance across cultures with special attention to issues of migration, cultural appropriation, gender and sexuality, and spiritual and religious expression. Students will also learn basic elements of participant observation research. Guest artists will teach different dance forms. No prior dance experience is necessary.
Instructors: Judith Hamera
Introduction to Genomics and Computational Molecular Biology
This interdisciplinary course provides a broad overview of computational and experimental approaches to decipher genomes and characterize molecular systems. We focus on methods for analyzing "omics" data, such as genome and protein sequences, gene expression, proteomics and molecular interaction networks. We cover algorithms used in computational biology, key statistical concepts (e.g., basic probability distributions, significance testing, multiple hypothesis correction, data evaluation), and machine learning methods which have been applied to biological problems (e.g., hidden Markov models, clustering, classification techniques).
Instructors: Joshua Akey, Claire McWhite, Mona Singh
Introduction to Genomics and Computational Molecular Biology
This interdisciplinary course provides a broad overview of computational and experimental approaches to decipher genomes and characterize molecular systems. We focus on methods for analyzing "omics" data, such as genome and protein sequences, gene expression, proteomics and molecular interaction networks. We cover algorithms used in computational biology, key statistical concepts (e.g., basic probability distributions, significance testing, multiple hypothesis correction, data evaluation), and machine learning methods which have been applied to biological problems (e.g., hidden Markov models, clustering, classification techniques).
Instructors: Joshua Akey, Claire McWhite, Mona Singh
Introduction to Latino/a/x Studies
This is an introductory survey of critical topics, themes, and approaches to the interdisciplinary field of Latin@x Studies. Drawing from anthropology, sociology, history, literature, critical race studies, gender and sexuality studies, this course will analyze the role and position of Latin@x in the United States alongside the policies and practices of the US in the Caribbean and Latin America. The course will explore questions of citizenship, immigration, imperialism, settler/colonialism, border crossing/borderlands, mass incarceration, policing, globalization, and other emerging formations of latinidad from a transnational perspective.
Introduction to Machine Learning
Provides a broad introduction to different machine learning paradigms and algorithms, providing a foundation for further study or independent work in machine learning, artificial intelligence, and data science. Topics include linear models for classification and regression, support vector machines, neural networks, clustering, principal components analysis, Markov decision processed, planning, and reinforcement learning.
Instructors: Ruth Fong, Karthik Narasimhan
Introduction to Pre-20th Century Black Diaspora Art
This course focuses on the networks, the imaginaries and the lives inhabited by Black artists, makers, and subjects from the 18th through 19th centuries. It revolves around the Caribbean (particularly the Anglophone Caribbean), North America and Europe. We will reflect on how pre-twentieth century Black artists are written into history or written out of it. We will explore the aesthetic innovation of these artists and the visionary worlds they created, and examine their travels, their writings, along with the social worlds and communities they formed. The course incorporates lectures and readings and, if possible, museum visits.
Instructors: Anna Kesson
Introduction to Programming Systems
Developing programming skills for systems building, including modular program design, programming style, testing, debugging, and performance tuning. Additionally, an introduction to ARM assembly language and machine language.
Instructors: Christopher Moretti
Introduction to Quantum Computing
This course will introduce the matrix form of quantum mechanics and discuss the concepts underlying the theory of quantum information. Some of the important algorithms will be discussed, as well as physical systems which have been suggested for quantum computing.
Instructors: Hakan Türeci

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