Neural network

Unsupervised learning

Hierarchical Clustering

K-means clustering

Dimensionality Reduction

Decision Trees

Markov Decision Process (MDP)

Principal Component Analysis (PCA)

Support Vector Machine (SVM)

Graph neural network (GNN)

Supervised learning

Backpropagation

Value iteration

Gradient descent

Linear regression

Exponential family

Clustering

Regularization (mathematics)

Overfitting

Kernel (operating system)

Reinforcement learning (RL)

Boosting

Logistic Regression

Apache Spark

Frequent Itemsets Mining

Locality-Sensitive Hashing

Recommender Systems

PageRank

Community Detection in Graphs

Graph Representation Learning

Learning Embeddings

Mining Data Streams

Matrix Sketching

Computational Advertising

Learning through Experimentation

Optimizing Submodular Functions

CS246: Mining Massive Data Sets Stanford University Spring 2023 This course focuses on data mining and machine learning algorithms for large scale data analysis. The emphasis is on parallel algorithms with tools like MapReduce and Spark. Topics include frequent itemsets, locality sensitive hashing, clustering, link analysis, and large-scale supervised machine learning. Familiarity with Java, Python, basic probability theory, linear algebra, and algorithmic analysis is required. ### What is this course about? [[Info Handout](https://web.stanford.edu/class/cs246/handouts/CS246_Info_Handout.pdf)]

The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis will be on MapReduce and [Spark](http://spark.apache.org/) as tools for creating parallel algorithms that can process very large amounts of data.

**Topics include**: Frequent itemsets and Association rules, Near Neighbor Search in High Dimensional Data, Locality Sensitive Hashing (LSH), Dimensionality reduction, Recommendation Systems, Clustering, Link Analysis, Large-scale Supervised Machine Learning, Data streams, Mining the Web for Structured Data, Web Advertising.  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 (e.g., CS107 or CS145 or equivalent are recommended).
- Good knowledge of Java and Python will be extremely helpful since most assignments will require the use of Spark.
- Familiarity with basic probability theory (CS109 or Stat116 or equivalent is sufficient but not necessary).
- Familiarity with writing rigorous proofs (at a minimum, at the level of CS 103).
- Familiarity with basic linear algebra (e.g., any of Math 51, Math 103, Math 113, CS 205, or EE 263 would be much more than necessary).
- Familiarity with algorithmic analysis (e.g., CS 161 would be much more than necessary). ### Reference Text

The following text is useful, but not required. It can be downloaded for free, or purchased from Cambridge University Press.

[Leskovec-Rajaraman-Ullman: Mining of Massive Dataset](http://www.mmds.org/)

CS246: Mining Massive Data Sets

Feature (machine learning)

Basis Functions

Cross-validation (statistics)

Linear classification

Kernel-based Classification

Principal Component Analysis

Singular Value Decomposition (SVD)

Latent Factor Models

Planning

Policy 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

Decision Making

Online Learning

PAC Learning

Mathematical Optimization

Stochastic gradient descent (SGD)

Error Decomposition

Multiclass Learning

Similarity Learning

Neural Networks Learning

COS 324 - Introduction to Machine Learning Princeton University Fall 2017 A thorough introduction to machine learning principles such as online learning, decision making, gradient-based learning, and empirical risk minimization. It also explores regression, classification, dimensionality reduction, ensemble methods, neural networks, and deep learning. The course material is self-contained and based on freely available resources. The course provides an introduction to machine learning.

**Topic covered**:

- Online learning and decision making
- Learning from examples and generalization
- Empirical risk minimization and regularization
- Introduction to convex analysis
- Gradient-based learning
- Implementation and analysis of learning algorithms for regression, binary classification, multiclass categorization, and ranking problems
- Dimensionality reduction methods
- Ensemble methods and boosting
- Neural networks and deep learning
- Markov decision precesses    **NOTICE:**  All material of the course is self-contained and based on freely available books and surveys.   
Main references:

- [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/), by Shai Shalev-Shwartz and Shai Ben-David
- [Online convex optimization](http://ocobook.cs.princeton.edu/), by Elad Hazan
- [Machine Learning](http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html), by Tom Mitchell
- An Introduction to Computational Learning Theory, by Michael Kearns &amp; Umesh Vazirani
- Machine Learning: A Probabilistic Perspective, by Kevin Murphy,

Further advanced references: - [Convex Optimization](http://stanford.edu/~boyd/cvxbook/), by Stephen Boyd and Lieven Vandenberghe
- [Convex optimization: algorithms and complexity](https://arxiv.org/abs/1405.4980), by Sebastien Bubeck
- Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig

Python Tutorials - [An interactive python tutorial](https://learnpython.org/) from LearnPython.com
- [Tutorial for Python 2.7](https://docs.python.org/2.7/tutorial/) from python.org
- [Tutorial for Python 3](https://docs.python.org/3/tutorial/) from python.org

COS 324 - Introduction to Machine Learning

Linear Algebra

Weighted Least Squares

Newton's Method

Generalized Linear Models

Multi-class Classification

Probability

Gaussian Discriminant Analysis

Python

NumPy

Bias - Variance

Feature / Model Selection

Gaussian Mixture Models (GMM)

Expectation Maximization

Factor Analysis

Independent Component Analysis (ICA)

Self-supervised Learning

Large Language Model (LLM)

In-context Learning

Prompting

Instruct Tuning

Reinforcement Learning from Human Feedback (RLHF)

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

Probability theory

Bayesian network

Undirected Models

Learning Bayes Nets

Exact Inference

Message Passing

Sampling

MAP Inference

Structured prediction

Parameter Learning

Bayesian Learning

Structure Learning

Variational Inference

CS 228 - Probabilistic Graphical Models Stanford University Winter 2023 An in-depth study of probabilistic graphical models, combining graph and probability theory. Equips students with the skills to design, implement, and apply these models to solve real-world problems. Discusses Bayesian networks, exact and approximate inference methods, etc. Probabilistic graphical models are a powerful framework for representing complex domains using probability distributions, with numerous applications in machine learning, computer vision, natural language processing and computational biology. Graphical models bring together graph theory and probability theory, and provide a flexible framework for modeling large collections of random variables with complex interactions. This course will provide a comprehensive survey of the topic, introducing the key formalisms and main techniques used to construct them, make predictions, and support decision-making under uncertainty.

The aim of this course is to develop the knowledge and skills necessary to design, implement and apply these models to solve real problems. The course will cover: (1) Bayesian networks, undirected graphical models and their temporal extensions; (2) exact and approximate inference methods; (3) estimation of the parameters and the structure of graphical models.  Students are expected to have background in basic probability theory, statistics, programming, algorithm design and analysis. If you are able to comfortably able to complete homework 1 then you likely have all the relevant background knowledge. ## Recommended Readings

**Corresponding Textbook**: (“PGM”) *Probabilistic Graphical Models: Principles and Techniques* by Daphne Koller and Nir Friedman. MIT Press.

**Course Notes**: Available [here](https://ermongroup.github.io/cs228-notes/). Student contributions welcome!

**Lecture Videos**: [here](https://canvas.stanford.edu/courses/166637/external_tools/3367)

**Further Readings**:

- (“GEV”) *Graphical models, exponential families, and variational inference* by Martin J. Wainwright and Michael I. Jordan. Available [online](https://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf).
- *Modeling and Reasoning with Bayesian Networks* by Adnan Darwiche. Available [online](http://ebookcentral.proquest.com/lib/stanford-ebooks/detail.action?docID=424583) (through Stanford).
- *Pattern Recognition and Machine Learning* by Chris Bishop. Available [online](https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book).
- *Machine Learning: A Probabilistic Perspective* by Kevin P. Murphy. Available [online](http://ebookcentral.proquest.com/lib/stanford-ebooks/detail.action?docID=3339490) (through Stanford).
- *Information Theory, Inference, and Learning Algorithms* by David J. C. Mackay. Available [online](http://www.inference.org.uk/mackay/itila/book.html).
- *Bayesian Reasoning and Machine Learning* by David Barber. Available [online](http://www.cs.ucl.ac.uk/staff/d.barber/brml/).

CS 228 - Probabilistic Graphical Models

Machine learning focuses on the development of algorithms and statistical models that can enable computers to learn from and make predictions or decisions without being explicitly programmed. Common sub-topics include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. 

Machine Learning

Computer Science

Machine Learning

Prerequisites

Common concepts