Multimodal Data

Inverse Transform Sampling

Open Sound Control (OSC)

Super-resolution Fluorescence Imaging

Text Processing

Fourier Transform

Video processing

Differential privacy

Prophet inequality

Digital Hypertext Pioneers

3D modeling

Normal-form game

Ligand Docking

Scheduling algorithms

The Myerson mechanism

Beat detection

Music grammars

Transformer (machine learning model)

Keynesian beauty contest

World Wide Web

Music robots

Myerson–Satterthwaite theorem

Virtual Screening

Synchronization (computer science)

Transfer, Exchange, and Storage of Hypertext Systems

Federated learning

Audio plug-ins

Surveillance Capitalism

Incomplete-information game

Deep learning

Concurrency

Multimodal Models

Genomics

Collaborative Systems

k-anonymity

Annotation

Audio editing software

Unit-Demand Valuations

Machine Learning Methods in Biomolecular Structure Prediction

Myerson's Payment Characterization

AI & Computational Biology

Image Analysis

Publication Culture & Peer Review

MIDI (Musical Instrument Digital Interface)

Societal Implications of Hypertext Systems

RNA Structure

Notetaking Systems

Music information retrieval

First-price Auctions

Hyperlink

Molecular Dynamics Simulation

Cryo-Electron Microscopy

Standard MIDI File format

Object detection

Hypertext Forerunners

Regular and MHR Distributions

Addressing & Permanence in Hypertext Systems

Image Segmentation

Musical notation

Diffusion

Artificial intelligence

Machine learning for music

Hypertext

Generalized second-price auction

Ethics of AI and Healthcare

Virtual time

Crawford-Knoer (CK) Auction

Sensors and instruments

X-Ray Crystallography

Semantic Web

Order Statistics

Convolution

Tracking of Individuals

Interpretability (machine learning)

Molecular Conformation

Nash equilibrium

Private aggregation of teacher ensembles (PATE)

Research Career Paths

Electronic health record

Medical imaging

Distributed learning

Bulow Klemperer Theorem

Temporal Media

Microscopy

Vickrey–Clarke–Groves (VCG) mechanism

Internet Architecture and Functioning

Health care

Auction Design Goals

Game theory

Max/MSP/Jitter

Revenue Equivalence

Discrete event simulation

NumPy

AI & Sustainability

Search/Filtering/Viewspecs in Hypertext Systems

Energy Functions

Literature Review

TensorFlow

Clock synchronization

Zettelkasten Method

Metaverse as Hypermedia

CS1951V Hypertext/Hypermedia Brown University Fall 2022 CS1951-V dives deep into the history and potential future of hypertext systems, providing insights beyond the World Wide Web. Through hands-on projects, students not only learn about hypertext but also gain expertise in full-stack application development using the modern MERN stack. The course blends technical skills with discussions, pushing students to prototype future hypermedia systems. CSCI1951-V, “Hypertext/Hypermedia: The Web Was Not the Beginning and the Web Is Not the End,” looks at hypertext systems that came before and after the World Wide Web as a basis for discussing what next generation hypertext systems could look like. Students will be doing writing assignments, reading, annotating, and writing technical papers, participating in in-class brainstorming sessions, discussing materials in a roundtable format, and developing software prototypes with Javascript and the state-of-the-art MERN (MongoDB, Express, React and Node.js) stack. Students will learn not only about hypertext, but will use that knowledge to develop full-stack applications using modern technologies and high-level software architectures as they prototype systems of the future. The course will be capped at 35 students to facilitate participation and discussion.  - Understand...
    - ...the foundations of the ubiquitous computing environment you have grown up with
    - ...the scope of hypermedia systems – from personal information manager to full multi-user, local to global
    - ...the features from early systems/research...
        - ...that have made it into today’s environment
        - ...that have not made it into today’s environment and why not?
    - ...the technical/architectural issues of hypermedia systems and…
        - ...how they have evolved over the years,
        - ...how some old assumptions have held back improvements for the future,  
        - ...how current technology can drive those improvements.
    - ...tradeoffs - state-of-technology, time vs. features vs. quality, etc. – what is/was the right choice?
    - ...the applications of hypertext
    - ...the societal implications of hypertext systems
- Learn...
    - ...to do a critical analysis of technical papers
    - ...to do comparisons of systems
    - ...to create crisp summaries and presentations of research and projects/products
    - ... to use the MERN stack
- Build...
    - ...a hypertext “corpus” using Dash, a modern hypermedia system being developed in Prof. van Dam’s research group 
    - ...a hypertext system as a group project
 This is a course for undergraduates and graduate students with CS systems background, object-oriented design and programming knowledge, and software architecture and development experience. Students must have taken at least an introductory CS sequence or equivalent, and preference will be given to those who have taken additional relevant systems courses or who have had significant experience in software development through jobs or internships. Experience with Javascript or Typescript and full-stack development is not required but is a plus. Interest in reading journal articles and papers, and writing, commenting, and presenting on those readings is essential.  

CS1951V Hypertext/Hypermedia

Auction theory

Bayesian game

Revelation Principle

Welfare Maximization

Myerson's Optimal Auction

Posted-Price Mechanisms

Approximation algorithm

Iterative Auction

Combinatorial auction

Ascending Auction

Walrasian equilibrium

CSCI 1440/2440 Topics in Algorithmic Game Theory Brown University Spring 2023 Offered by Brown University, this course intertwines game theory and computational considerations. With emphasis on strategic agent behavior, system design, and computational tractability, students delve into auction theory, bidding strategies, computational advertising, and automated negotiation. Knowledge in Java, Python, and certain mathematical areas is essential. This course examines topics in game theory and mechanism design through the lens of computation. Like such a course in an economics department, the focus is the design and analysis of systems utilized by self-interested agents. Students investigate how the potential for strategic agent behavior can/should influence system design, and the ramifications of conflicts of interest between system designers and participating agents. Unlike a traditional economics course, however, emphasis on computational tractability is paramount, so that simplicity may trump other design desiderata. Students will learn to analyze competing designs using the tools of theoretical computer science. They will also use artificial intelligence to build autonomous agents for computational advertising markets, wireless spectrum auctions, automated negotiation, and prediction markets.

There are two primary learning outcomes intended for students taking this course. Students should be able to:

- Reason about the design of multiagent systems taking into account agents' incentives, a perspective borrowed from economists, as well as computational performance, the bread and butter of computer scientists.
- Design and build effective autonomous agents for market domains that again incorporate both strategic and computational considerations.

  Mathematical maturity and programming experience are necessary for success in this course. The following are specific areas in which basic expertise is assumed, along with suggested courses for acquiring said expertise.

- Comfort with continuous mathematics: e.g., Math 0180, Math 0350, APMA 0350, or APMA 0360.
- Comfort with probability and statistics: e.g., CS 1450, APMA 1650, APMA 1655, or Math 1620.
- Comfort writing proofs: e.g., CS 22, CS 1010, CS 1550, or any 1000-level Math class.
- Comfort with programming: e.g., CS 4, CS 111, CS 15, CS 17, CS 19, or equivalent.

Some knowledge of Java and Python is assumed; neither language is taught.

For students wishing to enroll in the graduate section of this course, CSCI 2440, knowledge of Markov decision processes and linear programming is also assumed.

This course has no formal prerequisites. However, a sufficient level of mathematical maturity is required, as is knowledge of programming. For the most part, labs and homework assignments will be in Python, while the final project will be in Java. All assignments are programmed in pairs. If you worry that your programming skills are not up to snuff, take this opportunity to pair with someone who can help you get up to speed.  ### Topics

1. What is game theory?
1. What is mechanism design?
1. What is auction theory?
1. Simple, awesome, and EPIC auctions
1. Bidding strategies for combinatorial auctions
1. Empirical game-theoretic analysis and mechanism design
1. Application domains
    1. Computational advertising markets
    1. Wireless spectrum markets
    1. Automated negotiation
    1. Prediction markets

CSCI 1440/2440 Topics in Algorithmic Game Theory

Music 253 / CS 275a Symbolic Musical Information Stanford University Winter 2023 This preparatory course discusses symbolic frameworks in music-information, advanced notation systems, and data file structures. It's geared towards preparing for a course focused on computational musical analysis. Prerequisites include reading standard music notation and understanding tonal music theory. This music-information introduction surveys symbolic frameworks and methods for a wide range of applications. Areas covered include advanced notation systems, optical music recognition, data conversion and synchronization, and the internal structure of data files. The course is preparatory for Music 254/CS 275B (Computational Musical Analysis), which focuses on projects requiring a working knowledge of notation and familiarity with sound software.   Ability to read standard music notation. Knowledge of central concepts of tonal music theory (see [Tutorial](https://esf.ccarh.org/MusicTheory_Tutorials/MusicTheory_ComputerApps.htm)).  

Music 253 / CS 275a Symbolic Musical Information

Human-Computer Interaction (HCI)

What is research?

Research Contributions

Vectoring in Research

Velocity in Research

Evaluation in Research

Writing a paper

CS 197 Computer Science Research Stanford University Fall 2022 This course offers a team-based introduction to Computer Science research, aiming to produce a publishable work-in-progress. Research areas include AI, HCI, and Systems. Enrollment requires an application, and knowledge of CS106B is necessary. This course offers a team-based introduction to Computer Science research experiences. Students form teams within a desired research area (AI, HCI, Systems), then are matched with a quarter-long research project under direct mentorship of a Ph.D. student, with the goal of producing a publishable work-in-progress (e.g., poster) by the end of the quarter. Classes will be organized into a once-weekly lecture and a one-weekly section meeting. Research areas meet regularly as sections led by a Ph.D. student in the area, sharing progress and challenges. Lecture components of the course teach research concepts in computer science.  This is a 4-unit course. Enrollment requires an application, linked on the homepage. CS106B is required. Team projects will involve programming. 

CS 197 Computer Science Research

Classification

Self-supervised Learning

Unsupervised learning

Reinforcement learning (RL)

Fairness (machine learning)

CS 271 / BIOMEDIN 220 Artificial Intelligence in Healthcare Stanford University Fall 2022-2023 Offered by Stanford University, this course focuses on AI applications in healthcare, exploring deep learning models for image, text, multimodal, and time-series data in the healthcare context. Topics also address AI integration challenges like interpretability and privacy. Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare.  Proficiency in Python, or significant experience with a different programming language and ability to self-learn. Python will be used for homework assignments and the course project. Basic familiarity with college calculus (e.g. Math 19 or 41, comfortable taking derivatives), linear algebra (e.g. Math 51 or EE 103 / CME 103, comfortable with common matrix vector operations and notation), and probability and statistics (e.g. CME 106 or CS 109, comfortable with common probability distributions, mean, standard deviation, etc). Familiarity with machine learning, e.g. comfortable with the framework of machine learning and experience training a machine learning model. Familiarity with deep learning is highly recommended, e.g. prior experience training a deep learning model. 

CS 271 / BIOMEDIN 220 Artificial Intelligence in Healthcare

This is an umbrella topic that covers all interdisciplinary Computer Science subjects. Common interdisciplinary subjects include Computational Biology, Robotics, Computer Arts and so forth.

Interdisciplinary

Computer Science

Interdisciplinary

Prerequisites

Common concepts