Linear classification

Supervised learning

Regularization (mathematics)

Unsupervised learning

Latent Factor Models

Policy Iteration

Feature (machine learning)

Hierarchical Clustering

Reinforcement learning (RL)

Linear regression

Cross-validation (statistics)

Planning

K-means clustering

Basis Functions

Kernel-based Classification

Principal Component Analysis

Markov Decision Process (MDP)

Overfitting

Neural network

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

Machine Learning

Histories of AI

Societal impacts of AI

Vector (mathematics and physics)

Dot product

Geometric interpretations

Taking gradients

Discrete random variables

Probability distributions

Mean

Variance

Marginal distributions

Conditional distribution

Big-O notation

Computational complexity

Recurrence relation

Dynamic programming

Continuous optimization

Objective functions

Gradient descent

Machine learning

Loss minimization

Hinge loss

Equitable performance

Design and organization of features

Computation graphs

Deep learning models composition

Search problem

Breadth-first search (BFS)

Uniform cost search (UCS)

UCS heuristics

Transportation problem

Discounting factor

Model-free Monte Carlo

Q-learning

Function approximation

Game theory

Minimax algorithm

Temporal difference (TD) learning

Variable-based models

Backtracking search

Gibbs sampling

Bayesian network

Laplace smoothing

Logic

Entailment

Satisfiability

Soundness

Modus ponens

First-order logic

Unification (computer science)

Python

Stochastic gradient descent (SGD)

Non-linear functions

Backpropagation

Generalization

Exhaustive search

Depth-first search (DFS)

UCS correctness

Relaxed search problems

Dice game

Policy evaluation

Model-based Monte Carlo

State–action–reward–state–action (SARSA)

Epsilon-greedy exploration

Deep Reinforcement Learning

Halving game

Alpha-beta pruning

Nash equilibrium

Factor graphs

AC-3 algorithm

Markov random field

Hidden Markov Model (HMM)

Maximum Likelihood Estimation (MLE)

Propositional calculus (Propositional logic)

Contradiction

Model checking

Completeness

Horn clauses

Substitution (logic)

Skolem functions

CS 221 Artificial Intelligence: Principles and Techniques Stanford University Autumn 2022-2023 Stanford's CS 221 course teaches foundational principles and practical implementation of AI systems. It covers machine learning, game playing, constraint satisfaction, graphical models, and logic. A rigorous course requiring solid foundational skills in programming, math, and probability. The goal of artificial intelligence (AI) is to tackle complex real-world problems with rigorous mathematical tools. In this course, you will learn the foundational principles and practice implementing various AI systems. Specific topics include machine learning, search, Markov decision processes, game playing, constraint satisfaction, graphical models, and logic.  This course is fast-paced and covers a lot of ground, so it is important that you have a solid foundation in a number of areas. Here are the basic skills that you need and the classes that teach those skills:

- Programming (ideally Python): [CS 106A](http://www.stanford.edu/class/cs106a/), [CS 106B](http://www.stanford.edu/class/cs106b/), [CS 107](http://www.stanford.edu/class/cs107/)
- Discrete math, mathematical rigor: [CS 103](http://www.stanford.edu/class/cs103/)
- Probability: [CS 109](http://www.stanford.edu/class/cs109/)
- Linear algebra: [Math 51](https://web.stanford.edu/class/math51/textbook.html)

It is less important that you know particular things (e.g., we don't use eigenvectors in this course even though that's a pillar of any linear algebra course), and more important that you've done enough related things that you feel at ease with it. While it is possible to fill in the gaps, this course does move quickly, and ideally you want to be focusing your energy on learning AI rather than catching up on prerequisites. We have made a few [prerequisite modules](https://stanford-cs221.github.io/autumn2022/modules) that you can review to refresh your memory, and the first homework (foundations) will allow you to also get some practice on these basics. ### Further Reading

There are no required textbooks for this class, and you should be able to learn everything from the lecture notes and homeworks. However, if you would like to pursue more advanced topics or get another perspective on the same material, here are some great resources:

- [Russell and Norvig. Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu/). A comprehensive reference for all the AI topics that we will cover.
- [Koller and Friedman. Probabilistic Graphical Models](http://mitpress.mit.edu/books/probabilistic-graphical-models). Covers factor graphs and Bayesian networks (this is the textbook for CS228).
- [Sutton and Barto. Reinforcement Learning: An Introduction](https://mitpress.mit.edu/books/reinforcement-learning). Covers Markov decision processes and reinforcement learning (free online).
- [Hastie, Tibshirani, and Friedman. The Elements of Statistical Learning](https://web.stanford.edu/~hastie/ElemStatLearn/). Covers machine learning from a rigorous statistical perspective (free online).
- [Tsang. Foundations of Constraint Satisfaction](http://www.bracil.net/edward/fcs.html). Covers constraint satisfaction problems (free online).

Note that some of these books use different notation and terminology from this course, so it may take some effort to make the appropriate connections.

CS 221 Artificial Intelligence: Principles and Techniques

Artificial Intelligence

Linear classifier

Linear classification

2 courses cover this concept

COS 324: Introduction to Machine Learning

CS 221 Artificial Intelligence: Principles and Techniques