Unsupervised learning

Memory hierarchy

Matrix Multiplication

Shared Memory Parallelism

Roofline and Performance Modeling

Sources of Parallelism and Locality

Communication-avoiding matrix multiplication

Data Parallel Algorithms

CUDA (Compute Unified Device Architecture)

Graphics Processor (GPU)

Distributed Memory Machines and Programming

Advanced MPI

Collective Communication Algorithms

UPC++: Partitioned Global Address Space Languages

Domain Specific Languages (Halide)

Distributed Data Structures (BCL)

Parallel Matrix Multiply

Dense Linear Algebra

Sparse-Matrix-Vector-Multiplication

Iterative Solvers

Structured Grids

Machine learning

Supervised learning

Deep learning

Graph Partitioning

Fast Fourier transform

Graph Algorithms

Cloud Computing

Big Data Processing

High Performance Computing (HPC)

Dynamic Load Balancing

Hierarchical Methods for the N-Body Problem

Sorting algorithm

Search algorithm

Computational Biology

Quantum Computing

CS 267: Applications of Parallel Computers UC Berkeley Spring 2020 The course addresses programming parallel computers to solve complex scientific and engineering problems. It covers an array of parallelization strategies for numerical simulation, data analysis, and machine learning, and provides experience with popular parallel programming tools. CS267 was originally designed to teach students how to program parallel computers to efficiently solve challenging problems in science and engineering, where very fast computers are required either to perform complex simulations or to analyze enormous datasets. CS267 is intended to be useful for students from many departments and with different backgrounds, although we will assume reasonable programming skills in a conventional (non-parallel) language, as well as enough mathematical skills to understand the problems and algorithmic solutions presented. CS267 satisfies part of the course requirements for the Designated Emphasis ("graduate minor") in [Computational Science and Engineering](http://www.google.com/url?q=http%3A%2F%2Fcitris-uc.org%2Finitiatives%2Fdecse%2F&sa=D&sntz=1&usg=AOvVaw1fRFGvIaIYhkwE3kp4J1nf).

While this general outline remains, a large change in the computing world started in the mid 2000's: not only are the [fastest computers](http://www.google.com/url?q=http%3A%2F%2Fwww.top500.org%2F&sa=D&sntz=1&usg=AOvVaw1SiByfUjOpTR6ctURAV2Ef) parallel, but nearly all computers are becoming parallel, because the physics of semiconductor manufacturing will no longer let conventional sequential processors get faster year after year, as they have for so long (roughly doubling in speed every 18 months for many years). So all programs that need to run faster will have to become parallel programs. (It is considered very unlikely that compilers will be able to automatically find enough parallelism in most sequential programs to solve this problem.) For background on this trend toward parallelism, click [here](http://www.google.com/url?q=http%3A%2F%2Fwww.eecs.berkeley.edu%2FPubs%2FTechRpts%2F2006%2FEECS-2006-183.html&sa=D&sntz=1&usg=AOvVaw2XoPTheTzKVlyjp1G_YcM5).

Students in CS267 will get an overview of the parallel architecture space, gain experience using some of the most popular parallel programming tools, and be exposed to a number of open research questions. The lectures will also cover a broad set of parallelization strategies for applications covering numerical simulation and data analysis to machine learning.   

CS 267: Applications of Parallel Computers

Parallelism and Distributed Systems

Linear Algebra

Gaussian Discriminant Analysis

Support Vector Machine (SVM)

Regularization (mathematics)

Expectation Maximization

Prompting

Exponential family

Weighted Least Squares

Generalized Linear Models

Kernel (operating system)

Neural network

Feature / Model Selection

Factor Analysis

Self-supervised Learning

Instruct Tuning

Newton's Method

Probability

NumPy

Bias - Variance

Gaussian Mixture Models (GMM)

Independent Component Analysis (ICA)

In-context Learning

Multi-class Classification

Python

Backpropagation

K-means clustering

Principal Component Analysis (PCA)

Large Language Model (LLM)

Reinforcement Learning from Human Feedback (RLHF)

Logistic Regression

CS 229: Machine Learning Stanford University Winter 2023 This comprehensive course covers various machine learning principles from supervised, unsupervised to reinforcement learning. Topics also touch on neural networks, support vector machines, bias-variance tradeoffs, and many real-world applications. It requires a background in computer science, probability, multivariable calculus, and linear algebra.  This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.  Students are expected to have the following background:

- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy. (CS106A or CS106B, CS106X.)
- Familiarity with probability theory. (CS 109, MATH151, or STATS 116) 
- Familiarity with multivariable calculus and linear algebra (relevant classes include, but not limited to MATH 51, MATH 104, MATH 113, CS 205, CME 100.) Stanford Math 51 course text can be found [here](https://web.stanford.edu/class/math51/stanford/math51book.pdf). 

CS 229: Machine Learning

Machine Learning

Latent Factor Models

Policy Iteration

Feature (machine learning)

Linear classification

Hierarchical Clustering

Reinforcement learning (RL)

Linear regression

Cross-validation (statistics)

Planning

Basis Functions

Kernel-based Classification

Principal Component Analysis

Markov Decision Process (MDP)

Overfitting

Singular Value Decomposition (SVD)

Value iteration

COS 324: Introduction to Machine Learning Princeton University Spring 2019 This introductory course focuses on machine learning, probabilistic reasoning, and decision-making in uncertain environments. A blend of theory and practice, the course aims to answer how systems can learn from experience and manage real-world uncertainties.  This course provides a broad introduction to machine learning, probabilistic reasoning and decision making in uncertain environments. The course should be of interest to undergraduate students in computer science, applied mathematics, sciences and engineering, and lower-level graduate students looking to gain an introduction to the tools of machine learning and probabilistic reasoning with applications to data-intensive problems in the applied sciences, natural sciences and social sciences.

For students with interests in the fundamentals of machine learning and probabilistic artificial intelligence, this course will address three central, related questions in the design and engineering of intelligent systems. How can a system process its perceptual inputs in order to obtain a reasonable picture of the world? How can we build programs that learn from experience? How can we design systems to deal with the inherent uncertainty in the real world?

Our approach to these questions will be both theoretical and practical. We will develop a mathematical underpinning for the methods of machine learning and probabilistic reasoning. We will look at a variety of successful algorithms and applications. We will also discuss the motivations behind the algorithms, and the properties that determine whether or not they will work well for a particular task.  Students should be comfortable with writing non-trivial programs in Python. Students should have a background in basic probability theory, and some level of mathematical sophistication, including calculus and linear algebra. There is no required textbook for the course. This course has its own notes that are considered the required reading. Nevertheless, people learn in different ways and seeing the material presented in different formats can be valuable. To that end, additional optional material is linked on the course website and several books provide useful additional reading:

- Kevin Murphy. *Machine Learning: A Probabilistic Perspective*. MIT Press. 2012.
- Christopher M. Bishop. *Pattern Recognition and Machine Learning*. Springer. 2011.
- David J.C. MacKay. *Information Theory, Inference, and Learning Algorithms*. Cambridge University
Press. 2003. Freely available online at http://www.inference.org.uk/itila/book.html.
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman. *The Elements of Statistical Learning*.
Springer. 2001. Freely available online at http://www-stat.stanford.edu/~tibs/ElemStatLearn/
- Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. *An Introduction to Statistical
Learning*. Springer. 2013. Freely available online at http://www-bcf.usc.edu/~gareth/ISL/
- Richard S. Sutton and Andrew G. Barto. *Reinforcement Learning: An Introduction*. MIT Press. 1998.
Freely available online at http://incompleteideas.net/book/the-book-2nd.html

COS 324: Introduction to Machine Learning

Artificial intelligence

Health care

Medical imaging

Classification

Object detection

3D modeling

Video processing

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)

Federated learning

Image Segmentation

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

Decision Tree Learning

Margins

Naive Bayes

Maximizing Conditional Likelihood

Kernelizing Algorithms

Primal and Dual Forms

Finite Hypothesis Classes

k-fold cross-validation

Convolutional neural network (CNN)

Objective-Based Clustering

Semi-Supervised Learning

Learning Linear Separators

Maximum Likelihood Estimation (MLE)

Bag of Words

Computer Vision

Generalization and Overfitting

Model Selection and Regularization

Minimizing Squared Error

Deep Networks

Adaboost

Learning Representations

Active Learning

Co-training

Q-learning

Conditional Independence

Gradient descent

Kernelizing Perceptron

Kernelizing SVM

VC Dimension Based Bounds

Boosting

Interactive Learning

Transductive SVM

Bayes' theorem

Perceptron

Maximum A Posteriori (MAP)

Geometric Margins

Sample Complexity

Structural Risk Minimization

Maximizing Data Likelihood

Dimensionality Reduction

Sampling Bias

Convolution

10-401 Introduction to Machine Learning Carnegie Mellon University Spring 2018 A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.  Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as decision tree learning, Support Vector Machines, neural networks, boosting, statistical learning methods, unsupervised learning, active leaerning, and reinforcement learning. Short programming assignments include hands-on experiments with various learning algorithms.   - Machine Learning, Tom Mitchell. (optional)
- Pattern Recognition and Machine Learning, Christopher Bishop. (optional)
- Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, [available online](https://ebookcentral.proquest.com/lib/cm/detail.action?docID=3339490), (optional)

10-401 Introduction to Machine Learning

K-Nearest Neighbors

Linear Classifiers

Stochastic gradient descent (SGD)

Momentum

AdaGrad

Adam

Learning Rate Schedules

Softmax Loss

Multi-layer Perceptron

Higher-level representations

Image Features

Pooling

Batch Normalization

Transfer learning

AlexNet, VGG, GoogLeNet, ResNet

Activation Functions

Data Processing

Weight initialization

Hyperparameter Tuning

Data Augmentation

Feature visualization and inversion

Adversarial Examples

DeepDream and Style Transfer

Single-stage detectors

Two-stage detectors

Semantic/Instance/Panoptic segmentation

Long Short-Term Memory (LSTM)

Gated recurrent unit (GRU)

Language Modeling

Image Captioning

Sequence-to-sequence (Seq2Seq)

Object Detection

Self-Attention

Video classification

3D CNNs

Two-stream networks

Multimodal Video Understanding

Pixel RNN, Pixel CNN

Variational autoencoder (VAE)

Generative adversarial network (GAN)

Pretext Tasks

CS231n: Deep Learning for Computer Vision Stanford University Spring 2022 This is a deep-dive into the details of deep learning architectures for visual recognition tasks. The course provides students with the ability to implement, train their own neural networks and understand state-of-the-art computer vision research. It requires Python proficiency and familiarity with calculus, linear algebra, probability, and statistics. Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into the details of deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Additionally, the final assignment will give them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice. Through multiple hands-on assignments and the final course project, students will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks.  - Proficiency in Python  
    All class assignments will be in Python (and use numpy) (we provide a tutorial [here](http://cs231n.github.io/python-numpy-tutorial/) for those who aren't as familiar with Python). If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Javascript) you will probably be fine.
- College Calculus, Linear Algebra (e.g. MATH 19 or 41, MATH 51)  
     You should be comfortable taking derivatives and understanding matrix vector operations and notation.
- Basic Probability and Statistics (e.g. CS 109 or other stats course)  
    You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc. 

CS231n: Deep Learning for Computer Vision