Courses

What do the robots in ancient Greek mythology have to tell us about today’s AI? How did slavery shape how Greeks and Romans imagined autonomous tools? Where does artificial intelligence come from, and why do we tell the stories we do about what it can do and how it will change the world?
This course offers an introduction to the intellectual history of classical antiquity and a critical examination of artificial intelligence in the current cultural and political moment. Students in the course will learn about a topic in ancient Greek and Roman thought — stories about autonomous tools — and how that topic relates to social history and culture in the ancient world. They will then use that knowledge to frame questions about artificial intelligence and robots in present society, and examine critical approaches to the large generative models that are garnering so much attention today. The goal is equip students with a) a basic familiarity with how ancient Greek and Roman thought relates to its cultural context, b) an analytical framework for approaching claims about technology in historical and present contexts, and c) an appreciation for how humanistic inquiry can answer urgent questions in their lives.
Prerequisites: none
The course is intended for students for little or no familiarity with the study of the ancient world, and as an introduction to the study of ancient Greece and Rome. Familiarity with texts encountered in the fall semesters of Literature Humanities or Contemporary Civilizations will be helpful, but is neither required or presumed on the part of the instructor.

This course looks at the narrative and the historical context for an extraordinary event: the conquest of the Persian empire by Alexander III of Macedonia, conventionally known as “Alexander the Great”. We will explore the different worlds Alexander grew out of, confronted, and affected: the old Greek world, the Persian empire, the ancient near-east (Egypt, Levant, Babylonia, Iran), and the worlds beyond, namely pre-Islamic (and pre-Silk Road) Central Asia, the Afghan borderlands, and the Indus valley. The first part of the course will establish context, before laying out a narrative framework; the second part of the course will explore a series of themes, especially the tension between military conquest, political negotiation, and social interactions. Overall, the course will serve as an exercise in historical methodology (with particular attention to ancient sources and to interpretation), an introduction to the geography and the history of the ancient world (classical and near-eastern), and the exploration of a complex testcase located at the contact point between several worlds, and at a watershed of world history.

Pushing back against this trope of homelessness, this course illuminates the robust, vibrant, and multifacetted
qualities of a home in the Diaspora, lasting for over a millennium, that both Ashkenazi and Sephardi
Jews managed to create for themselves in lands, predominantly populated by Slavs. They did so despite the
many constraints of legal and religious discrimination, threats of physical violence, displacement, and countless
forms of exclusion from dominant society. Moving across centuries, countries, and languages, we will revisit the
contributions of the Jews to their so called “host cultures” by way of diverse media—literary and non-fictional
works, memoirs, artistic works, songs, feature and documentary films, journalistic pieces, and more. By the end
of this journey, we will have gained a deeper understanding of the ways in which the Jews and Slavs have been
intimately imbricated and intertwined since times immemorial.

All course materials are available in English. No reading knowledge of Russian or other Slavic languages
is required. Course participants with the reading knowledge of any region-specific language are encouraged to
consult the respective originals, provided by the instructor upon request. This course will be of interest to those
majoring in Slavic and/or Jewish Studies, as well as anyone interested in Comparative Literature, History, Art
History, and Film and Visual Studies.

A general introduction to computer science for science and engineering students interested in majoring in computer science or engineering. Covers fundamental concepts of computer science, algorithmic problem-solving capabilities, and introductory Java programming skills. Assumes no prior programming background. Columbia University students may receive credit for only one of the following two courses: 1004 or 1005.

Logic and formal proofs, sequences and summation, mathematical induction, binomial coefficients, elements of finite probability, recurrence relations, equivalence relations and partial orderings, and topics in graph theory (including isomorphism, traversability, planarity, and colorings).

Regular languages: deterministic and non-deterministic finite automata, regular expressions. Context-free languages: context-free grammars, push-down automata. Turing machines, the Chomsky hierarchy, and the Church-Turing thesis. Introduction to Complexity Theory and NP-Completeness.

Mathematical foundations of machine learning: Linear algebra, multivariable calculus,
and probability and statistics. Comprehensive review and additional treatment of
relevant topics used in the analysis and design of machine learning models. Preliminary
exposure to core algorithms such as linear regression, gradient descent, principal
component analysis, low-rank approximations, and kernel methods.

Prior knowledge of Python is recommended. Provides a broad understanding of the basic techniques for building intelligent computer systems. Topics include state-space problem representations, problem reduction and and-or graphs, game playing and heuristic search, predicate calculus, and resolution theorem proving, AI systems and languages for knowledge representation, machine learning and concept formation and other topics such as natural language processing may be included as time permits.

Prior knowledge of Python is recommended. Provides a broad understanding of the basic techniques for building intelligent computer systems. Topics include state-space problem representations, problem reduction and and-or graphs, game playing and heuristic search, predicate calculus, and resolution theorem proving, AI systems and languages for knowledge representation, machine learning and concept formation and other topics such as natural language processing may be included as time permits.

Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.

Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.