Courses

• CAS CL 497: Directed Study in Latin
  
• CAS CL 502: Studies in Ancient Greek and Roman Literature
  Topics vary. Open to advanced undergraduate and graduate students. The literature studied is read in its original language, either ancient Greek or Latin, so advanced skill in the appropriate language is required. Please contact the instructor if you are unsure whether you have the appropriate knowledge of the language. Topic for Fall 2016: Reading Thucydides in Greek. This seminar involves a close reading of Thucydides' History of the Peloponnesian War. In studying selections of this greatest and most difficult historian of antiquity, our focus is heavily philological -- grammar, syntax, style, and translation. Also offered as CAS CL 461 A1.
• CAS CL 520: Studies in Latin Literature
  Topic for Spring 2016: The Aeneid's Reception. This course expands upon the examination of Vergil's Aeneid begun in CAS CL 451/502 A1 in Fall 2015, and examines the epic's later influence.
• CAS CL 522: Survey of Latin Literature II
  Survey of Latin authors focusing on the period of the early Empire; introduction to classical scholarship. For advanced students wishing to increase their language skills through extensive reading.
• CAS CL 530: Latin Prose Composition
  Practice in set and free composition of Latin prose, aimed at developing advanced language proficiency.
• 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.
• 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.
• 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.
• CAS CN 510: Principles and Methods of Cognitive and Neural Modeling I
  Explores psychological, biological, mathematical, and computational foundations of behavioral and brain modeling. Topics include organizational principles, mechanisms, local circuits, network architectures, cooperative and competitive non-linear feedback systems, associative learning systems, and self-organizing code-compression systems. The adaptive resonance theory model unifies many course themes. CAS CN 510 and 520 may be taken concurrently.
• CAS CN 530: Neural and Computational Models of Vision
  Current models of mammalian visual processes are constrained by experimental and theoretical results from psychology, physiology, computer science, and mathematics. The course evaluates the explanatory adequacy of competing neural and computational models of such processes as edge detection, textural grouping, shape-from-shading, stereopsis, motion detection, and color perception. Students perform computer simulations of some of the examined models.
• CAS CN 570: Neural and Computational Models of Conditioning, Reinforcement, Motivation, and Rhythm
  Develops neural and computational models of how humans and animals learn to successfully predict environmental events and generate behavioral actions that satisfy internally defined criteria of success or failure. Reinforcement learning and its homeostatic (drive, arousal, rhythm) and nonhomeostatic (reinforcement) modulators are analyzed in depth. Recognition learning and recall learning networks are joined to the reinforcement learning network to analyze how these several processes cooperate to generate successful goal-oriented behavior. Maladaptive behaviors and certain mental disorders are analyzed from a unified theoretical perspective. Applications to the design of freely moving adaptive robots are noted.
• CAS CS 101: Introduction to Computing
  The computer is presented as a tool that can assist in solving a broad spectrum of problems. This course provides a general introduction designed to dispel the mystery surrounding computers and introduces the fundamental ideas of programs and algorithms. (Does not count for CS major or minor credit.) Carries MCS divisional credit in CAS.
• CAS CS 103: Introduction to Internet Technologies and Web Programming
  Introduction to the basic architecture and protocols underlying the operation of the Internet with an emphasis on Web design, Web application programming, and algorithmic thinking. General familiarity with the Internet is assumed. (Does not count for CS major credit.) Carries MCS divisional credit in CAS.
• CAS CS 105: Introduction to Databases and Data Mining
  General introduction to computational methods for processing collection of data. Topics include databases and data modeling; writing simple programs to process data; data mining and data visualization. Applications are drawn from business, the arts, the life sciences, and social sciences. (Does not count for CS major credit.) Carries MCS divisional credit in CAS.
• CAS CS 108: Introduction to Applications Programming
  Introduction to programming suitable for students not majoring in computer science. Topics include: application decomposition and abstraction; data manipulation and programming with databases; graphical user interfaces; debugging. Assignments include applications in science, commerce, games, the Web, and social networking. (Does not count for CS major credit.) Carries MCS divisional credit in CAS.
• CAS CS 109: The Art and Science of Quantitative Reasoning
  Buying music on-line, making phone calls, predicting the weather, or controlling disease outbreaks would be impossible without mathematics, statistics, and computer science. Focuses on methods of reasoning common to these disciplines, and how they enable the modern world. (Does not count for CS major credit.) Also offered as CAS MA 109. Carries MCS divisional credit in CAS.
• CAS CS 111: Introduction to Computer Science 1
  The first course for computer science majors and anyone seeking a rigorous introduction. Develops computational problem-solving skills by programming in the Python language, and exposes students to variety of other topics from computer science and its applications. Carries MCS divisional credit in CAS.
• CAS CS 112: Introduction to Computer Science 2
  Covers advanced programming techniques and data structures. Topics include recursion, algorithm analysis, linked lists, stacks, queues, trees, graphs, tables, searching, and sorting. Carries MCS divisional credit in CAS.
• CAS CS 131: Combinatoric Stuctures
  Representation, analysis, techniques, and principles for manipulation of basic combinatoric structures used in computer science. Rigorous reasoning is emphasized.
• CAS CS 132: Geometric Algorithms
  Basic concepts, data structures, and algorithms for geometric objects. Examples of topics: Cartesian geometry, transformations and their representation, queries and sampling, triangulations. Emphasis on rigorous reasoning and analysis, advancing algorithmic maturity and expertise in its application.

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