Markov chain Monte Carlo (MCMC)

Python

Independence (probability theory)

Random variable

Joint Discrete Distributions

Correlation

Multinomial Distribution

Beta distribution

Hypothesis Testing

Multi-Armed Bandits

Probability via Simulation

Discrete random variables

Continuous Random Variables

Joint Continuous Distributions

Multivariate Normal Distribution

Discrete Time Stochastic Process

Dirichlet Distribution

Bootstrapping

Markov's inequality

Convolution

Discrete Probability

Expectation

The Normal Distribution

Conditional distribution

Moment Generating Functions

Order Statistics

Maximum a Posteriori Estimation

Confidence Intervals

The Chernoff Bound

Maximum Likelihood Estimation (MLE)

Counting

Conditional probability

Variance

Transforming Random Variables

Covariance

Limit Theorems

Central Limit Theorem

Method of Moments Estimation

Properties of Estimators

Credible Intervals

Chebyshev‘s inequality

CSE 312 Foundations of Computing II University of Washington Winter 2022 This course dives deep into the role of probability in the realm of computer science, exploring applications such as algorithms, systems, data analysis, machine learning, and more. Prerequisites include CSE 311, MATH 126, and a grasp of calculus, linear algebra, set theory, and basic proof techniques. Concepts covered range from discrete probability to hypothesis testing and bootstrapping. ## Why is CSE 312 Important?

While the initial foundations of computer science began in the world of discrete mathematics (after all, modern computers are digital in nature), recent years have seen a surge in the use of probability as a tool for the analysis and development of new algorithms and systems. As a result, it is becoming increasingly important for budding computer scientists to understand probability theory, both to provide new perspectives on existing ideas and to help further advance the field in new ways.

Probability is used in a number of contexts, including analyzing the likelihood that various events will happen, better understanding the performance of algorithms (which are increasingly making use of randomness), or modeling the behavior of systems that exist in asynchronous environments ruled by uncertainty (such as requests being made to a web server). Probability provides a rich set of tools for modeling such phenomena
and allowing for precise mathematical statements to be made about the performance of an algorithm or a system in such situations.

Furthermore, computers are increasingly often being used as data analysis tools to glean insights from the enormous amounts of data being gathered in a variety of fields; you’ve no doubt heard the phrase “big data” referring to this phenomenon. Probability theory and statistics are the foundational methods used for designing new algorithms to model such data, allowing, for example, a computer to make predictions about new or uncertain events. In fact, many of you have already been the users of such techniques. For example, most email systems now employ automated spam detection and filtering. Methods for being able to automatically infer whether or not an email message is spam are frequently rooted in probabilistic methods. Similarly, if you have ever seen online product recommendation (e.g., “customers who bought X are also likely to buy Y”), you’ve seen yet another application of probability in computer science. Even more subtly, answering detailed questions like how many buckets you should have in your a hash table or how many machines you should deploy in a data center (server farm) for an online application make use of probabilistic techniques to give precise formulations based on testable assumptions.
 Our goal in this course is to build foundational skills and give you experience in the following areas:

1. **Understanding the combinatorial nature of problems**: Many real problems are based on understanding the multitude of possible outcomes that may occur, and determining which of those outcomes satisfy some criteria we care about. Such an understanding is important both for determining how ikely an outcome is, but also for understanding what factors may affect the outcome (and which of those may be in our control).
2. **Working knowledge of probability theory and some of the key results in statistics**: Having a solid knowledge of probability theory and statistics is essential for computer scientists today. Such knowledge includes theoretical fundamentals as well as an appreciation for how that theory can be successfully applied in practice. We hope to impart both these concepts in this class.
3. **Appreciation for probabilistic statements**: In the world around us, probabilistic statements are often made, but are easily misunderstood. For example, when a candidate in an election is said to have a 53% likelihood of winning does this mean that the candidate is likely to get 53% of the vote, or that that if 100 elections were held today, the candidate would win 53% of them? Understanding the difference between these statements requires an understanding of the model in the underlying probabilistic analysis.
4. **Applications in machine learning and theoretical computer science**: We are not studying probability theory simply for the joy of drawing summation symbols (okay, maybe some people are, but that’s not what we’re really targeting in this class), but rather because there are a wide variety of applications where probability and statistics allow us to solve problems that might otherwise be out of reach (or would be solved more poorly without the tools that probability and statistics can bring to bear). We’ll look at examples of such applications throughout the class. For example, machine learning is a quickly growing subfield of artificial intelligence which has grown to impact many applications in computing. It focuses on analyzing large quantities of data to build models that can then be harnessed in real problems, such as filtering email, improving web search, understanding computer system performance, predicting financial markets, or analyzing DNA. Probability and statistics form the foundation of these systems. Another example application area is the use of randomized algorithms and probabilistic data structures. These usually have simpler and more elegant implementations than their deterministic counterparts, and have more efficient time and/or space complexity. We will be learning about some of these applications and you will have the opportunity to implement some of these algorithms.
 CSE 311 and MATH 126. Here is a quick rundown of some of the mathematical tools we’ll
be using in this class: calculus (integration and differentiation), linear algebra (basic operations on vectors and matrices), an understanding of the basics of set theory (subsets, complements, unions, intersections, cardinality, etc.), and familiarity with basic proof techniques (including induction). - (Recommended) Alex Tsun, Probability & Statistics with Applications to Computing, 2020. Available free online [here](http://www.alextsun.com/files/Prob_Stat_for_CS_Book.pdf).
- (Optional) Sheldon Ross, A First Course in Probability (10th Ed.), Pearson Prentice Hall, 2018.
- (Optional) Larsen and Marx, An Introduction to Mathematical Statistics (5th edition). Prentice-Hall.
- (Optional) Dimitri P. Bertsekas and John N. Tsitsiklis, Introduction to Probability, First Edition, Athena Scientific, 2000. Available free online [here](https://www.vfu.bg/en/e-Learning/Math--Bertsekas_Tsitsiklis_Introduction_to_probability.pdf).

CSE 312 Foundations of Computing II

Mathematical Foundations

Theoretical Computer Science

Property-preserving lossy compression

Nearest Neighbor

k-d tree

Regularization (mathematics)

L1 regularization

Matrix compression

Laplacian of a graph

Reservoir sampling

Majority elements

Distance-preserving compression

PAC Guarantees

Linear-Algebraic Techniques

De-noising

Eigenvectors/eigenvalues

The Fourier Perspective

Linear programming

Dimensionality Reduction

Approximate heavy hitters

Locality-Sensitive Hashing (LSH)

MinHash

Principal Component Analysis (PCA)

Matrix completion

Spectral embeddings

Convex optimization

Empirical risk minimization

Bloom filters

Jaccard Similarity

Polynomial embedding

Maximizing variance

Uniqueness of low-rank tensor factorizations

Graph coloring

Importance Sampling

Sparse Vector/Matrix Recovery

Privacy Preserving Computation

Count-min sketch

Euclidean Distance

Johnson-Lindenstrauss Transform

Random projection

Power iteration algorithm

Jenrich's algorithm

Spectral clustering

Markov Chains

Compressed Sensing

Differential privacy

Similarity Search

Lp distance

Generalization

L2 regularization

Singular Value Decomposition (SVD)

Spectral Graph Theory

Interpretations of the second eigenvalue

Stationary distributions

Mathematical Programming

CS 168: The Modern Algorithmic Toolbox Stanford University Spring 2022 CS 168 provides a comprehensive introduction to modern algorithm concepts, covering hashing, dimension reduction, programming, gradient descent, and regression. It emphasizes both theoretical understanding and practical application, with each topic complemented by a mini-project. It's suitable for those who have taken CS107 and CS161.  This course will provide a rigorous and hands-on introduction to the central ideas and algorithms that constitute the core of the modern algorithms toolkit. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. The course will be structured as a sequence of one-week investigations; each week will introduce one algorithmic idea, and discuss the motivation, theoretical underpinning, and practical applications of that algorithmic idea. Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Topics include modern techniques in hashing, dimension reduction, linear and convex programming, gradient descent and regression, sampling and estimation, compressive sensing, and linear-algebraic techniques (principal components analysis, singular value decomposition, spectral techniques).  CS107 and CS161, or permission from the instructor. 

CS 168: The Modern Algorithmic Toolbox

Max-product algorithm

Sum-product algorithm on factor graphs

Learning graphical models

Directed graphical models

Density evolution

Causal structure discovery

Undirected graphical models

Gaussian graphical models

Relations between graphical models

Gaussian Belief Propagation

Belief propagation

Variational methods

CSE 515 Statistical methods in computer science University of Washington Winter 2021 It emphasizes inference in engineering settings, utilizing the powerful language of probabilistic graphical models. This course provides a good blend of probability theory, graph theory, and computation. This course focuses on how to address inference in complex engineering settings. While driven by applications, the course is not about these applications. Rather, it emphasizes a common foundation and conceptual framework for inference-related questions arising in various fields including, but not limited to, machine learning, signal processing, artificial intelligence, computer vision, control, and communication. We focus on inference: learning about the hidden state of the world that we care from available observations. We rely on the powerful language of probabilistic graphical models to leverage on the inherent structure of the given problem and efficiently perform inference. Graphical models build upon the beautiful marriage between probability theory and graph theory and use graphs to capture the fundamental structure of multivariate statistical models and also design efficient computation for inference tasks.  Students entering the class should be comfortable with programming and should have a pre-existing working knowledge of linear algebra (e.g., MATH 308), probability and statistics (e.g., CSE 312/STAT390), and algorithms. For a brief refresher, we recommend that you consult the statistics/probability reference materials on the Textbooks page. ### Textbook and reference materials

There is no existing text that perfectly matches the content of CSE515. However, the following references contain useful additional details and insights. Email the instructor if you are interested in further reading materials.

- D. Koller & N. Friedman, [Probabilistic Graphical Models: Principles and Techniques](https://mitpress.mit.edu/books/probabilistic-graphical-models), MIT Press, 2009
- Steffen L. Lauritzen, [Graphical Models](http://www.amazon.com/Graphical-Models-Oxford-Statistical-Science/dp/0198522193), Oxford University Press, 1996
- Marc Mezard and Andrea Montanari, [Information, Physics, and Computation](http://www.stanford.edu/~montanar/RESEARCH/book.html), Oxford University Press, 2009
- M. Wainwright and M. Jordan, [Graphical models, exponential families, and variational inference](https://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf), Foundations and Trends in Machine Learning, 2008

CSE 515 Statistical methods in computer science

Polynomial Identity Testing

Karger's algorithm

Karger-Stein algorithm

Primality Testing

Sampling-based median algorithm

Moment generating functions

Chernoff bound

Randomized routing on the hypercube

Balls and Bins

"Poissonization"

The power of 2 choices

Metric Embeddings

Bourgain's embedding

Johnson-Lindestrauss Transform

Dimension reduction and sparsity

Restricted Isometry Property (RIP)

Ramsey's theorem

Independent Sets

De-randomization via Conditional Expectations

Second-moment method

Algorithmic Lovász local lemma

Randomized algorithm for 2SAT

Fundamental Theorem of Markov Chains

Mixing times

Strong stationary times

Coupling

Martingales

the Doob Martingale

Azuma-Hoeffding tail bounds

The martingale stopping theorem

Pseudorandomness: expanders and extractors

CS 265 / CME 309 Randomized Algorithms and Probabilistic Analysis Stanford University Fall 2022 This course dives into the use of randomness in algorithms and data structures, emphasizing the theoretical foundations of probabilistic analysis. Topics range from tail bounds, Markov chains, to randomized algorithms. The concepts are applied to machine learning, networking, and systems. Prerequisites indicate intermediate-level understanding required. Randomness pervades the natural processes around us, from the formation of networks, to genetic recombination, to quantum physics. Randomness is also a powerful tool that can be leveraged to create algorithms and data structures which, in many cases, are more efficient and simpler than their deterministic counterparts. This course covers the key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Emphasis is on theoretical foundations, though we will apply this theory broadly, discussing applications in machine learning and data analysis, networking, and systems. Topics include tail bounds, the probabilistic method, Markov chains, and martingales, with applications to analyzing random graphs, metric embeddings, random walks, and a host of powerful and elegant randomized algorithms.  CS 161 and STAT 116, or equivalents and instructor consent. 

CS 265 / CME 309 Randomized Algorithms and Probabilistic Analysis