Federated learning

Artificial intelligence

Health care

Medical imaging

Classification

Object detection

3D modeling

Video processing

NumPy

TensorFlow

Electronic health record

Multimodal Data

Multimodal Models

Genomics

Interpretability (machine learning)

Fairness (machine learning)

Ethics of AI and Healthcare

Distributed learning

Differential privacy

k-anonymity

Private aggregation of teacher ensembles (PATE)

Deep learning

Transformer (machine learning model)

Unsupervised learning

Image Segmentation

Reinforcement learning (RL)

Self-supervised 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

Artificial Intelligence

Interdisciplinary

End-to-end encryption

Man-in-the-middle attacks

Public key infrastructure (PKI)

Merkle trees

Certificate Transparency

Anonymous messaging

Encrypted search

Bitcoin

Ethereum

Smart contracts

Algorand

Zero-knowledge proof

Rollups

Zero-knowledge smart contracts

Web3

Multi-Party Computation (MPC)

Cryptocurrency wallets

Backdoor-resilient authentication

CS 294-163: Secure Systems from Decentralized Trust UC Berkeley Fall 2022 This graduate seminar focuses on the development of secure systems built from decentralized trust, including end-to-end encryption systems and secure collaborative learning. It requires a solid introduction to cryptography and systems. Topics include blockchain, smart contracts, and zero-knowledge proofs, among others. This course is a graduate seminar on developing (secure) systems from decentralized trust.

In the past years, there has been much excitement in both academia and industry around the notion of decentralized security, which refers to, loosely speaking, security mechanisms that do not rely on the trustworthiness of any central entity. In only a few years, this area has generated many beautiful cryptographic constructs as well as a tremendous amount of real-world adoption, through blockchain, secure custody of secrets, secure collaborative learning – and now forming the technical backbone of the movement on “web3.0”.

The course will cover topics such as decentralized ledger systems, decentralized authentication and access control, end-to-end encryption systems, secure collaborative learning through secure multi-party computation, and others. For concrete learning, throughout the class we will develop a decentralized trust stack enabling common decentralized trust applications (3 units).  This is an advanced course, requiring a solid introduction to cryptography (CS161 or equivalent) and to systems (CS162 or equivalent). There is no required textbook for this class. You can refer to the [Berkeley CS161 textbook](https://textbook.cs161.org/) to refresh on security concepts and [A Graduate Course in Applied Cryptography](http://toc.cryptobook.us/) for cryptography.

CS 294-163: Secure Systems from Decentralized Trust

Blockchain

Computer Security and Cryptography

Federated learning

2 courses cover this concept

CS 271 / BIOMEDIN 220 Artificial Intelligence in Healthcare

CS 294-163: Secure Systems from Decentralized Trust