Classification

Information extraction

Words

Language models and smoothing

Lexicons

Morphology

Question Answering (QA)

Noisy channel models

Edit Distance

Part of speech tags

Sequence Tagging

Deep learning

Syntactic representations of natural language

Chomsky hierarchy and natural language

Treebanks

Lexical semantics

Vector semantics

Contextualized representations (BERT)

Verb / sentence semantics

Compositional semantics

Semantic Parsing

Discourse

Entity linking

Pragmatics

Speech

Word Embeddings

Natural Language Generation

Machine Translation

11-411/611 Natural Language Processing Carnegie Mellon University Spring 2021 Focused on computational systems for human languages, this course introduces various NLP applications, such as translation and summarization. It encompasses a broad scope, from machine learning to linguistics, with a software engineering perspective. This course is about a variety of ways to represent human languages (like English and Chinese) as computational systems, and how to exploit those representations to write programs that do neat stuff with text and speech data, like

- translation,
- summarization,
- extracting information,
- question answering,
- natural interfaces to databases, and
- conversational agents.

This field is called Natural Language Processing or Computational Linguistics, and it is extremely multidisciplinary. This course will therefore include some ideas central to Machine Learning and to Linguistics.

We'll cover computational treatments of words, sounds, sentences, meanings, and conversations. We'll see how probabilities and real-world text data can help. We'll see how different levels interact in state-of-the-art approaches to applications like translation and information extraction.

From a software engineering perspective, there will be an emphasis on rapid prototyping, a useful skill in many other areas of Computer Science. ### Should I take this course?

Yes, if:

- you're a CS student interested in languages, language technology, or information processing
- you're a CS student who needs an "applications" credit
- you're a language technology minor (this course is an elective option)
- you're a linguistics student who can write computer programs (this course is an elective option)
- you always suspected natural language was kind of like Lisp (or Java or ...)
- you want computers to take over the world
- you don't want computers to take over the world, but if they do, you want to negotiate your release
- you like AI, machine learning, and/or theoretical computer science, and want to apply them to a hard real-world problem CS courses on data structures and algorithms, and strong programming skills. [Speech and Language Processing](https://home.cs.colorado.edu/~martin/slp2.html) (2nd Edition, 2007, Prentice-Hall), by Daniel Jurafsky and James Martin

### FAQ
Should I take this course?

Yes, if:

- you're a CS student interested in languages, language technology, or information processing
- you're a CS student who needs an "applications" credit
- you're a language technology minor (this course is an elective option)
- you're a linguistics student who can write computer programs (this course is an elective option)
- you always suspected natural language was kind of like Lisp (or Java or ...)
- you want computers to take over the world
- you don't want computers to take over the world, but if they do, you want to negotiate your release
- you like AI, machine learning, and/or theoretical computer science, and want to apply them to a hard real-world problem

11-411/611 Natural Language Processing

Natural Language Processing

Histograms

Pivots

Models

P-Value

Confidence Intervals

Sample Means

Least Squares

Privacy

Bayes' theorem

Data type

Iteration 

Census

Function (computer programming)

Joins

Chance

Comparing Distributions

A/B Testing

Center and Spread

Correlation

Residuals

Conditional probability

Tables

Charts

Groups

Conditional (computer programming)

Sampling

Decisions

Causality

Normal distribution

Linear regression

Regression Inference

Classifiers

Data 8: The Foundations of Data Science UC Berkeley Fall 2022 UC Berkeley's course blends inferential thinking, computational thinking, and real-world relevance, offering students hands-on analysis of real-world datasets. It covers critical concepts in computer programming, statistical inference, privacy, and study design. Foundations of Data Science combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It also delves into social issues surrounding data analysis such as privacy and study design.  This course does not have any prerequisites beyond high-school algebra. The curriculum and format is designed specifically for students who have not previously taken statistics or computer science courses. Students with some prior experience in either statistics or computing are welcome to enroll and will find much of interest due to the innovative nature of the course. Students who have taken several statistics or computer science courses should instead take a more advanced course. Our primary text is an online book called [Computational and Inferential Thinking: The Foundations of Data Science](http://inferentialthinking.com/). This text was written for the course by the course instructors. A complete PDF of the textbook can be found in the Student Materials Google Drive.

The computing platform for the course is hosted at [data8.datahub.berkeley.edu](file:///Users/jweichert/Desktop/Data%208/data-8.github.io/fa22/data8.datahub.berkeley.edu). Students find it convenient to use their own computer for the course. If you do not have adequate access to a personal computer, we can help you borrow a machine; please contact data8@berkeley.edu.

Data 8: The Foundations of Data Science

Data Science

Artificial intelligence

Health care

Medical imaging

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)

Transformer (machine learning model)

Unsupervised learning

Federated 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

Supervised learning

Training/Validation/Testing

Perceptron

MNIST

Loss function

Optimization

Backpropagation

Automatic differentiation

Matrix representation of Neural Networks

Graphics Processor (GPU)

Multilayer perceptron

Activation Functions

Convolutional neural network (CNN)

Overfitting

Regularization (mathematics)

Language Models

Feedforward neural network

Recurrent neural network (RNN)

Long Short-Term Memory (LSTM)

Gated recurrent unit (GRU)

Attention (machine learning)

Scaling Deep Learning Systems

Interpretation of Neural Networks

Autoencoders

Variational autoencoder (VAE)

Generative adversarial network (GAN)

Deepfake

Value iteration

Deep Q-learning

Policy Gradient Methods

Actor-Critic Methods

Graph neural network (GNN)

CSCI 1470/2470 Deep Learning Brown University Spring 2022 Brown University's Deep Learning course acquaints students with the transformative capabilities of deep neural networks in computer vision, NLP, and reinforcement learning. Using the TensorFlow framework, topics like CNNs, RNNs, deepfakes, and reinforcement learning are addressed, with an emphasis on ethical applications and potential societal impacts. Over the past few years, Deep Learning has become a popular area, with deep neural network methods obtaining state-of-the-art results on applications in computer vision (Self-Driving Cars), natural language processing (Google Translate), and reinforcement learning (AlphaGo). These technologies are having transformative effects on our society, including some undesirable ones (e.g. deep fakes).

This course is there to give students a practical understanding of how Deep Learning works, how to implement neural networks, and how to apply them ethically. We introduce students to the core concepts of deep neural networks and survey the techniques used to model complex processes within the contexts of computer vision and natural language processing.

Throughout the course, we emphasize and require students to think critically about potential ethical pitfalls that can result from mis-application of these powerful models. The course is taught using the Tensorflow deep learning framework.
 By the end of this course, you will be able to:

- Learn about the fundamental algorithms that underly all modern deep learning models.
- Implement different types of deep learning models in Tensorflow.
- Think critically about using a deep learning model for a task and its potential societal impact.
- Collaborate with classmates on a team project to apply deep learning models to task of your
choice.
- Communicate your findings (both positive and negative results are encouraged) through pre-
sentations. - A basic programming course: (CSCI 0150, 0170 or 0190)
- A linear algebra course: (CSCI 0530, MATH 0520 or 0540)
- A stats / probability course: (CSCI 0220, 1450, 0450, MATH 1610, APMA 1650 or 1655)

Exceptions may be possible for those missing one of these prerequisites if (a) the student has taken
another course which covers similar material, or if (b) the student will be concurrently taking the
prerequisite. If either of these situations applies to you, use the “Request Override” feature in
Courses@Brown to request an override code (and explain why you believe your situation merits
one). ### Textbook

None required. Students are encouraged to refer to the following textbook, which is available online:

- [Deep Learning](https://www.deeplearningbook.org/), by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

CSCI 1470/2470 Deep Learning

Deep Learning

Classification

4 courses cover this concept

11-411/611 Natural Language Processing

Data 8: The Foundations of Data Science

CS 271 / BIOMEDIN 220 Artificial Intelligence in Healthcare

CSCI 1470/2470 Deep Learning