To see a complete list of recently offered courses and their descriptions, visit the online course catalog.
The courses listed below are provided by the JHU Public Course Search. This listing provides a snapshot of immediately available courses and may not be complete. Course registration information can be found on the Student Information Services (SIS) website.
| Course # (Section) | Title | Day/Times | Instructor | Location | Term | Course Details |
|---|---|---|---|---|---|---|
| AS.001.248 (01) | FYS: Who has an accent? Dialects of English | MW 12:00PM - 1:15PM | Renwick, Margaret | Krieger 134A | Fall 2025 | Language is at the heart of human interaction. What are the linguistic habits that unite or divide us? This First-Year Seminar introduces students to dialects of English speakers around the world. Students will explore the major properties that cross-cut different varieties of English, including regional or socially-driven accents of North America, the United Kingdom, New Zealand and Australia, as well as other World Englishes. Particular attention will be paid to pronunciation, so students will practice the International Phonetic Alphabet and learn acoustic analysis through hands-on activities, but discussion will also focus on dialectal differences in word choice, sentence structure, and linguistic meaning. We will engage with known and emerging varieties of English by drawing on academic sources, multimedia materials, and real-world experience. Who speaks with an accent? Everyone! |
| AS.001.253 (01) | FYS: The Drama of Artificial Intelligence | MW 10:30AM - 11:45AM | Flombaum, Jonathan; Gruenhut, Johanna | Gilman 277 | Fall 2025 | From the rise of machine consciousness to the ethics of automation, artificial intelligence has captured the human imagination. This First-Year Seminar explores how playwrights and theater artists engage with AI as a dramatic subject, a creative tool, and a lens for examining the human condition. Through an interdisciplinary approach, and co-taught by faculty in Psychological and Brain Sciences and Theatre, students will analyze plays and performances that grapple with the hopes and anxieties surrounding AI. Works such as Karel Čapek’s R.U.R. (which introduced the word “robot”), Jordan Harrison’s Marjorie Prime, Jennifer Haley’s The Nether, and Nick Payne’s Constellations will serve as key texts alongside contemporary plays such as Julia Cho’s The Language, Rolin Jone’s The Intelligent Design of Jenny Chow, The Effect by Lucy Prebble, and Photograph 51 by Anna Ziegler. We will also discuss experimental performances that integrate AI technologies. |
| AS.050.102 (01) | Language and Mind | TTh 10:30AM - 11:45AM | Yarmolinskaya, Julia | Shaffer 3 | Fall 2025 | Introductory course dealing with theory, methods, and current research topics in the study of language as a component of the mind. What it is to "know" a language: components of linguistic knowledge (phonetics, phonology, morphology, syntax, semantics) and the course of language acquisition. How linguistic knowledge is put to use: language and the brain and linguistic processing in various domains. |
| AS.050.105 (01) | Introduction to Cognitive Neuropsychology | TTh 12:00PM - 1:15PM | McCloskey, Michael E | Remsen Hall 101 | Fall 2025 | When the brain is damaged or fails to develop normally, even the most basic cognitive abilities (such as the ability to understand words, or perceive objects) may be disrupted, often in remarkable ways. This course explores a wide range of cognitive deficits, focusing on what these deficits can tell us about how the normal brain works. Topics include brain anatomy and causes of brain damage, reading and spelling deficits, unilateral spatial neglect, hemispheric disconnection, cortical plasticity, and visual perception of location and orientation. Students read primary sources: journal articles that report deficits and discuss their implications. |
| AS.050.116 (01) | Visual Cognition | MW 3:00PM - 4:15PM | Staff | Krieger 111 | Fall 2025 | How do humans make sense of the visual world around them? This course will provide an introductory survey of current research, methods, and theories in visual cognition. We will draw upon topics in cognitive psychology, cognitive neuroscience, cognitive neuropsychology, and artificial intelligence. |
| AS.050.236 (01) | Neurolinguistics | TTh 1:30PM - 2:45PM | Yarmolinskaya, Julia | Krieger 111 | Fall 2025 | This course provides an introductory survey of the cognitive neuroscience of language – a multidisciplinary field in the intersection of Linguistics, Psycholinguistics, and Neuroscience. We will explore current research on the neural bases of the perception, production, and acquisition or human language in neuro-typical and impaired individuals. |
| AS.050.308 (01) | Acoustic Phonetics | MW 1:30PM - 2:45PM | Renwick, Margaret | Krieger 134A | Fall 2025 | This course explores theories of speech sound generation in the human vocal system, in order to learn the relationships between discrete linguistic classes of sounds and their articulatory and acoustic manifestations. Foundations for these theories include an understanding of the anatomy employed during speech, as well as principles of airflow and pressure, which are united in the source-filter theory of speech production. As speech unfolds in time, the resulting acoustic signal is altered according to the vocal tract’s configuration, leading to characteristic acoustic manifestations for vowels and consonants. These phonetic cues, in turn, ground formal phonological representations via distinctive feature theory. The course includes a practical introduction to measurement of the acoustic correlates of speech sounds. |
| AS.050.312 (01) | Cognitive Neuroimaging Methods in High-Level Vision | MW 12:00PM - 1:15PM | Staff | Krieger 111 | Fall 2025 | This course is an advanced seminar and research practicum course. It will provide the opportunity to learn about fMRI methods used in the field of vision science and for students to have hands-on experience to develop, design and analyze a research study on topics in the cognitive neuroscience field of high-level vision. In the first part of the course students will read recent fMRI journal papers and learn about common fMRI designs and analysis methods; in the second part of the course students will conduct a research study to address a research question developed from readings. Students are expected to write a paper in a short journal article format at the end of the course and to present their results in front of the class. Research topics will vary but with special focus on topics in high-level visual processing. |
| AS.050.317 (01) | Semantics I | TTh 10:30AM - 11:45AM | Rawlins, Kyle | Krieger 111 | Fall 2025 | This is an introduction to the study of meaning in natural language. We address the conceptual and empirical issues in semantic theory and introduce some formal machinery that has been developed to deal with such problems. After discussing foundational questions, we turn to formal semantics and pragmatics, as well as their interfaces with syntax and the lexicon. Specific topics include presupposition, type-driven composition, quantification, lexical aspect, argument structure, and lexical representations of meaning. |
| AS.050.321 (01) | Syntax II | T 1:30PM - 4:00PM | Koval, Pasha | Krieger 134A | Fall 2025 | Building on AS.050.320, this course explores and compares generative syntactic theories developed over the past 35 years since the 1990s, with a particular focus on those couched within the Minimalist Program. We will use the Government and Binding theory (familiar from AS.050.320) as a useful first benchmark that minimalist theories are routinely trying to meet or beat in terms of empirical coverage. The main theoretical goal of this course is to show how different proposals to reduce the number of syntactic primitives reshape the architecture of grammar, leading to new theoretical developments, which in turn point to new empirical domains of inquiry. We will see how simplifying our theoretical models in a principled way leads to deeper generalizations about the structure of language. On the practical side, this course is designed to teach you syntactic argumentation. Developing the ability to evaluate and produce logically valid and well-articulated arguments is foundational for understanding and engaging with the current theoretical syntactic literature. A non-exhaustive list of topics for this course is as follows: the T-model and the demolition of d-structure and s-structure, alternative views of phrase structure (such as Bare Phrase Structure and Labeling), theories of phrasal movement (including copy theory and multidominance), theoretical approaches to syntactic well-formedness (e.g., Barriers and Harmonic Grammar), locality and minimality effects (e.g., phases and Relativized Minimality) , binding and anaphora, linearization and the syntax-prosody interface, and derivational economy and externalization. |
| AS.050.333 (01) | Psycholinguistics | MW 3:00PM - 4:15PM | Koval, Pasha | Krieger 134A | Fall 2025 | This course examines the cognitive and neural mechanisms that allow us to use linguistic knowledge to produce and understand language in real time. You will learn about the key findings on language perception, production, and acquisition, while gaining hands-on, laboratory-style experience with some of the methods commonly used to study language performance. The focus of the class is on the relation between experimental findings and linguistic theory, addressing two core questions of psycholinguistics: How is language organized and implemented in the brain? How (if at all) does our mental machinery shape natural language? Also available as AS.050.633. |
| AS.050.347 (01) | Deep Learning for Cognitive Neuroscience | TTh 12:00PM - 1:15PM | Isik, Leyla | Hodson 313 | Fall 2025 | Over the last decade, there has been amazing progress in deep learning AI systems for vision and language, and more and more cognitive neuroscientists are using these tools to study the human brain. This course will give an overview of popular deep learning methods, including convolutional neural networks, recurrent neural networks, variational autoencoders, and transformers, with the goal of addressing two key questions: 1) to what extent do these deep learning systems act like humans, and 2) what questions can they help us answer about the human mind and brain. The class will involve a mix of lectures, hands-on coding assignments, and reading/discussion of primary research articles. The course will focus heavily on vision but will include some topics in language (including large language models) and social cognition. |
| AS.050.348 (01) | First Language Acquisition | TTh 3:00PM - 4:15PM | Yarmolinskaya, Julia | Krieger 111 | Fall 2025 | This course provides an introduction to the fields of first and bilingual language acquisition by looking at questions such as the following: Can the grammar of a native language be learned solely on the basis of noticing statistical correlations among words? How does native language acquisition explain — or is explained by — the universal properties, shared by all languages, of words and grammars? How does being exposed to multiple languages from birth affect language acquisition and what happens when a child is not exposed to any language early in life? This is the first part of a two-part course sequence on first and second language acquisition. |
| AS.050.365 (01) | Cracking the code: Theory and modeling of information coding in neural activity | TTh 10:30AM - 11:45AM | Bonner, Mick | Krieger 134A | Fall 2025 | One of the most foundational concepts in neuroscience is the idea that neural activity encodes information about an animal’s sensory environment and internal mental states. This idea is closely connected to the concept of mental representation in cognitive science and philosophy, whereby the mind is proposed to contain internal symbols that represent things in the external world. There have been many fascinating discoveries about how neural signals encode information, but we are still far from a comprehensive theory of neural representation. Recent major developments in neuroscience and machine learning have opened up a new world of possibilities for investigating the underlying principles of information coding in the brains of humans and other animals. In this course, we will discuss primary research articles on neural representation and information processing, and students will implement computational analyses that address issues in these domains. We will mostly focus on vision as a system that illustrates broader principles of information processing in the human brain. The reading material will include work from philosophy, neuroimaging, electrophysiology, and computational modeling. The topics covered include mental and neural representation, neural tuning, population coding, information theory, encoding and decoding models, dimensionality reduction, computational models, deep learning, and other applications of machine learning in neuroscience. Enrollment is limited to Juniors and Seniors. While this class does not have formal prerequisites, programming experience (e.g., AS 250.205 Introduction to Computing) and mathematical preparation (e.g., AS.110.107 Calculus II) are essential. It is also highly recommended that students have previously taken introductory courses in cognitive or systems neuroscience (e.g., AS.050.203 Neuroscience: Cognitive) and machine learning or neural network modeling (e.g., AS.050.372 Foundations of Neural Network Theory). |
| AS.050.375 (01) | Probabilistic Models of the Visual Cortex | TTh 9:00AM - 10:15AM | Yuille, Alan L | Krieger 170 | Fall 2025 | The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modeling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. Also offered as EN.601.485. Required Background: Calculus I and experience in a programming language (Python preferred). |