TensorFlow

Markov Decision Process (MDP)

Learning-based grasping and manipulation

Shared autonomy

Model-based and model-free RL for robot control

Imitation learning

Grasping and Manipulation

Interaction-aware learning

Reinforcement learning (RL)

Interactive Perception

Neural network

Learning-based perception

Reinforcement Learning from Human Feedback (RLHF)

AA 174B / AA 274B / CS 237B / EE 260B Principles of Robot Autonomy II Stanford University Winter 2023 This course provides a deeper understanding of robot autonomy principles, focusing on learning new skills and physical interaction with the environment and humans. It requires familiarity with programming, ROS, and basic robot autonomy techniques. This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. Concepts that will be covered in the course are: Reinforcement Learning (RL) and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, as well as imitation learning and human intent inference. Students will learn the theoretical foundations for these concepts. With this course, students will obtain a fundamental understanding of
advanced principles of robot autonomy, including robot learning, physical interaction with
the environment, and interaction with humans. - Familiarity with ROS and basic techniques for robot autonomy (e.g., AA274A or
equivalent).
- Familiarity with programming (e.g., CS 106A or equivalent) and Python.
- College calculus, linear algebra (e.g., CME 100 or equivalent).
- Basic probability and statistics (e.g., CME 106 or equivalent).  There is no required textbook.

AA 174B / AA 274B / CS 237B / EE 260B Principles of Robot Autonomy II

Artificial Intelligence

Robotics

Python

NumPy

Logistic Regression

Batch Normalization

Generative adversarial network (GAN)

Shallow Neural Network

Deep Neural Network

Optimization Algorithms

Initialization

Regularization (mathematics)

Gradient Checking

Optimization

Hyperparameter Tuning

Deep Convolutional Models

Residual Networks

Detection Algorithms

Face Recognition

Neural Style Transfer

You Only Look Once (YOLO)

ConvNets

Image Segmentation

U-Net

Recurrent neural network (RNN)

Long Short-Term Memory (LSTM)

Natural Language Processing (NLP)

Word Embeddings

Attention (machine learning)

Transformer (machine learning model)

Deep Reinforcement Learning

Neural Machine Translation

Trigger Word Detection

Transfer learning

Sequence-to-sequence (Seq2Seq)

CS 230 Deep Learning Stanford University Fall 2022 An in-depth course focused on building neural networks and leading successful machine learning projects. It covers Convolutional Networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Students are expected to have basic computer science skills, probability theory knowledge, and linear algebra familiarity.  Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.  Students are expected to have the following background, and are invited to take the [Workera](http://www.workera.ai/) technical assessments prior to the class to self-assess themselves prior to taking the class:

- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. This corresponds to a Developing level (or badge) in the “Algorithmic Coding” section on [Workera](http://www.workera.ai/).
- Familiarity with the probability theory (CS 109 or STATS 116), which students can assess by taking the “Data Science” section on [Workera](http://www.workera.ai/).
- Familiarity with linear algebra (MATH 51), which students can assess by taking the “Mathematics” section on [Workera](http://www.workera.ai/). 

CS 230 Deep Learning

Deep Learning

Artificial intelligence

Health care

Medical imaging

Classification

Object detection

3D modeling

Video processing

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

Unsupervised learning

Federated learning

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

Interdisciplinary

Supervised learning

Linear regression

Training/Validation/Testing

Perceptron

MNIST

Loss function

Backpropagation

Automatic differentiation

Matrix representation of Neural Networks

Graphics Processor (GPU)

Multilayer perceptron

Activation Functions

Convolutional neural network (CNN)

Overfitting

Language Models

Feedforward neural network

Gated recurrent unit (GRU)

Machine Translation

Scaling Deep Learning Systems

Interpretation of Neural Networks

Autoencoders

Variational autoencoder (VAE)

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

TensorFlow

4 courses cover this concept

AA 174B / AA 274B / CS 237B / EE 260B Principles of Robot Autonomy II

CS 230 Deep Learning

CS 271 / BIOMEDIN 220 Artificial Intelligence in Healthcare

CSCI 1470/2470 Deep Learning