Courses

The listing of a course description here does not guarantee a course’s being offered in a particular term. Please refer to the published schedule of classes on the MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.

• CAS CL 530: Latin Prose Composition
  Close study of exemplary Latin prose as the basis for original composition in Greek of sentences and short passages, then more extensive prose pieces. Also, a study of prose style in an array of Roman authors. Not offered 2025/2026.
• CAS CL 561: Survey of Greek Literature 1
  Reading course designed to study the history of Greek literature through a chronological survey of representative authors and genres: Homer through the archaic age. Not offered 2025/2026.
• CAS CL 562: Survey of Greek Literature 2
  Reading course designed to study the history of Greek literature through a chronological survey of representative authors and genres: Classical through Hellenistic period. Instructed by Professor Steven Smith in Spring 2026.
• CAS CL 563: Greek Prose Composition
  Close study of exemplary Greek prose as the basis for original composition in Greek of sentences and short passages, then more extensive prose pieces. Also, a study of prose style in an array of Greek authors.
• CAS CL 717: GRK & ROMAN REL
  GRK & ROMAN REL
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  • Critical Thinking
  • Historical Consciousness
  • Social Inquiry I
• CAS CS 501: Computer Science Practicum
  Various applications of computer science that vary semester to semester. Please contact the CAS Computer Science Department for detailed descriptions.
• CAS CS 505: Introduction to Natural Language Processing
  Undergraduate Prerequisites: (CASCS365) - Natural language processing (NLP) is a field of AI which aims to equip computers with the ability to intelligently process natural (human) language. This course explores statistical and machine learning techniques for the automatic analysis of natural language data.
• CAS CS 506: Data Science Tools and Applications
  Undergraduate Prerequisites: CAS CS 108 or CAS CS 111; CAS CS 132 or CAS MA 242 or CAS MA 442. CAS CS 112 is recommended. - Covers practical skills in working with data and introduces a wide range of techniques that are commonly used in the analysis of data, such as clustering, classification, regression, and network analysis. Emphasizes hands-on application of methods via programming. Effective Fall 2019, this course fulfills a single unit in each of the following BU Hub areas: Research and Information Literacy, Teamwork/Collaboration.
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  • Research and Information Literacy
  • Teamwork/Collaboration
• CAS CS 511: Formal Methods 1
  Undergraduate Prerequisites: (CASCS320 OR CASCS330 OR CASCS350) - Introduction to formal specification, analysis, and verification of computer system behavior. Topics include formal logical reasoning about computer programs and systems, automated and semi-automated verification, and algorithmic methodologies for ascertaining that a software system satisfies its formally specified properties. Cannot be taken for credit in addition to the course with the same number formerly entitled "Object-Oriented Software Principles."
• CAS CS 516: Software Foundations via Formal Languages
  Undergraduate Prerequisites: (CASCS320 & CASCS330) or equivalent courses approved by the instructor. - Study of the hierarchy of formal languages with a focus on algorithms on the formalisms. Experimentation with these algorithms using a computer toolset. Applications of these algorithms to compiler construction, implementation of finite-state control devices, and formal methods.
• CAS CS 519: Spark! Software Engineering X-Lab Practicum
  Undergraduate Prerequisites: (CASCS 411 OR CDSDS 310) or equivalent experience in software development and consent of instructor. - Consent provided upon successful completion of pass/fail diagnostic test that assesses student readiness to take the course. This course offers students in computing disciplines the opportunity to apply their programming and system development skills by working on real-world projects provided from partnering organizations within and outside of BU, which are curated by Spark! The course offers a range of project options where students can improve their technical skills, while also gaining the soft skills necessary to deliver projects aligned to the partner's goals. These include teamwork and communications skills and software development processes. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Oral and/or Signed Communication, Teamwork/Collaboration.
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  • Ethical Reasoning
  • Oral and/or Signed Communication
  • Teamwork/Collaboration
• CAS CS 523: Deep Learning
  Prerequisites: CASCS 541 or CASCS 542 - Mathematical and machine learning background for deep learning. Feed-forward networks., Backpropagation. Training strategies for deep networks. Architectures such as convolutional, recurrent, transformer networks. Deep reinforcement and unsupervised learning. Exposure to modern programming tools and libraries. Other recent topics, time permitting.
• CAS CS 525: Compiler Design Theory
  Undergraduate Prerequisites: (CASCS 320) or consent of instructor. - Graduate Prerequisites: (CASCS 320 OR CASCS 332) - Covers the basic mathematical theory underlying the design of compilers and other language processors and shows how to use that theory in practical design situations. Topics may include lexical analysis, parsing, syntax-directed translation, code optimization, and code generation.
• CAS CS 528: Cloud Computing
  Undergraduate Prerequisites: (CASCS350 & CASCS460) or consent of instructor. CASCS455, CASCS552, and CASCS562 are recomme nded. - Fundamentals of cloud computing covering IaaS platforms, OpenStack, key Big Data platforms, and data center scale systems. Examines influential publications in cloud computing. Culminates in a group project supervised by a mentor from industry or academia.
• CAS CS 530: Advanced Algorithms
  Undergraduate Prerequisites: (CASCS330) or consent of instructor. - Graduate Prerequisites: (CASCS330) or consent of instructor. - Studies the design and efficiency of algorithms in several areas of computer science. Topics are chosen from graph algorithms, sorting and searching, NP-complete problems, pattern matching, parallel algorithms, and dynamic programming.
• CAS CS 531: Advanced Optimization Algorithms
  Undergraduate Prerequisites: CAS MA 123 & 124, or equivalent and CAS CS 132 or equivalent; or conse nt of instructor. - Optimization algorithms, highlighting the fruitful interactions between discrete and continuous. Intended audience is advanced master students and doctoral students. Topics include gradient descent algorithms, online optimization, linear and semidefinite programming, duality, network optimization, submodular optimization, approximation algorithms via continuous relaxations.
• CAS CS 535: Complexity Theory
  Undergraduate Prerequisites: (CASCS332) or consent of instructor. - Graduate Prerequisites: (CASCS332) - Covers topics of current interest in the theory of computation chosen from computational models, games and hierarchies of problems, abstract complexity theory, informational complexity theory, time-space trade-offs, probabilistic computation, and recent work on particular combinatorial problems.
• CAS CS 537: Randomness in Computing
  Undergraduate Prerequisites: (CASCS330 OR CASCS530) . CASCS535 is recommended or consent of instructor. - Graduate Prerequisites: CASCS330 or CASCS530 is recommended. - Survey of probabilistic ideas of the theory of computation. Topics may include Monte Carlo and Las Vegas probabilistic computations; average case complexity and analysis; random and pseudorandom strings; games and cryptographic protocol; information; inductive inference; reliability;others. (Offered alternate years.)
• CAS CS 538: Fundamentals of Cryptography
  Undergraduate Prerequisites: (CASCS131 & CASCS237 & CASCS357) or consent of instructor. - Graduate Prerequisites: (CASCS332) - Basic Algorithms to guarantee confidentiality and authenticity of data. Definitions and proofs of security for practical constructions. Topics include perfectly secure encryption, pseudorandom generators, RSA and Elgamal encryption, Diffie-Hellman key agreement, RSA signatures, secret sharing, block and stream ciphers.
• CAS CS 541: Applied Machine Learning
  Undergraduate Prerequisites: CS111 (CS112 recommended); CS132 or MA242 (or EK103); CS237 or MA581 ( or EK381.) CS365 is recommended. - Covers practical skills in machine learning including techniques for clustering, classification, regression, feature selection, and model compression. Emphasizes hands-on application of methods via programming on real- world datasets.