Probability

Linear Algebra

Gaussian Discriminant Analysis

Support Vector Machine (SVM)

Regularization (mathematics)

Expectation Maximization

Unsupervised learning

Prompting

Exponential family

Weighted Least Squares

Generalized Linear Models

Kernel (operating system)

Neural network

Feature / Model Selection

Factor Analysis

Self-supervised Learning

Instruct Tuning

Supervised learning

Newton's Method

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

Informed Search

Decision Networks

Perceptron

Search with Other Agents

Hidden Markov Model (HMM)

Optimization

Bayes' Nets: Independence

Constraint Satisfaction Problem (CSP)

Value of Information (VPI)

Bayes' Nets: Representation

Markov Decision Process (MDP)

Particle Filtering

Bayes' Nets: Inference

Uninformed Search

Reinforcement learning (RL)

Naive Bayes

Bayes' Nets: Sampling

CS 188 Introduction to Artificial Intelligence UC Berkeley Fall 2022 UC Berkeley's CS 188 course covers the basic ideas and techniques for designing intelligent computer systems, emphasizing statistical and decision-theoretic modeling. By the course's end, students will have built autonomous agents that can make efficient decisions in a variety of settings. This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.

By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.

See the syllabus for slides, deadlines, and the lecture schedule. Readings refer to [fourth edition of AIMA](http://aima.cs.berkeley.edu/) unless otherwise specified.  - **CS 61A or 61B**: Prior computer programming experience is expected (see below)
- **CS 70 or Math 55**: Familiarity with basic concepts of propositional logic and probability are expected (see below)

*CS61A AND CS61B AND CS70 is the recommended background.*

The required math background in the second half of the course will be significantly greater than the first half. The self-diagnostic assignment Homework 0 will help check your preparation.

Course programming assignments will be in Python. We do not assume that students have previous experience with the language, but we do expect you to learn the basics very rapidly. Project 0 is designed to teach you the basics of Python and how the CS 188 submission autograder works. Project 1 is a good representation of the programming level that will be required for subsequent projects in this class. 

CS 188 Introduction to Artificial Intelligence

Artificial Intelligence

Adversarial Search

Linear Programming

Integer Programming

Propositional calculus (Propositional logic)

First-order logic

Game Theory: Equilibrium

Contraint Satisfaction Problems

Agents and Search

Knowledge representation and reasoning

Classical Planning

Particle Filter

Game Theory: Social Choice

Logical Agents

Human Compatible AI

15-381 Artificial Intelligence Carnegie Mellon University Spring 2019 This course from Carnegie Mellon University provides a deep understanding of AI's theory and practice, covering methods for decision-making, problem-solving, and handling uncertainty. Topics include search algorithms, computational game theory, and AI ethics.  This course is about the theory and practice of Artificial Intelligence. We will study modern techniques for computers to represent task-relevant information and make intelligent (i.e., satisficing or optimal) decisions towards the achievement of goals. The search and problem-solving methods are applicable throughout a large range of industrial, civil, medical, financial, robotic, and information systems. We will investigate questions about AI systems such as: how to represent knowledge, how to effectively generate appropriate sequences of actions and how to search among alternatives to find optimal or near-optimal solutions. We will also explore how to deal with uncertainty in the world and how to learn from experience. We will cover the aggregation of conflicting preferences and computational game theory. Throughout the course, we will discuss topics such as AI and Ethics and introduce applications related to AI for Social Good. We expect that by the end of the course students will have a thorough understanding of the algorithmic foundations of AI, how probability and AI are closely interrelated, and how automated agents make decisions. We also expect students to acquire a strong appreciation of the big-picture aspects of developing fully autonomous intelligent agents.  The prequisite for this course is 15-122, which implies that:

1. You have taken two college level computer science courses and have the programming and computer science experience necessary for the course. Programming assignments will be in Python, which you are expected to know or learn the basics quickly.
1. You have taken the 15-122 corequisite either 21-127 Concepts of Mathematics or 15-151 Mathematical Foundations of Computer Science.

The corequisites for this course are 21-122 Integration and Approximation (or higher level calculus) and 21-241 Matrices and Linear Transformations (or 21-242). For each corequisite, you should either have completed it prior to starting 15-381 or have it on your schedule for Spring 2019.

Please see the instructors if you are unsure whether your background is suitable for the course. [Artificial Intelligence: A Modern Approach, Third Edition](http://aima.cs.berkeley.edu/) (recommended)

15-381 Artificial Intelligence

Structure Learning

Policy Search

Beliefs

Multiagent Reasoning

Inference

Exact Solutions

Actor Critic

Offline Belief State Planning

Parameter Learning

Online Planning

Model-Based and Model-Free Reinforcement Learning

Controller Abstractions

Simple Decisions

Policy Gradient Estimation and Optimization

Exact Belief State Planning

Policy Validation, Implications, and Summary

Representations

Bayesian Networks Representation and Inference

Approximate Value Functions

Exploration / Exploitation

Online Belief State Planning

AA 228 / CS 238 Decision Making under Uncertainty Stanford University Winter 2023 The course introduces decision making under uncertainty from a computational perspective, covering dynamic programming, reinforcement learning, and more. Prerequisites include basic probability and fluency in a high-level programming language. This course introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Topics include Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes. Applications cover air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration. - You will gain a broad fundamental understanding of the mathematical models and solution methods for decision making (exercises, three quizzes).
- You will be able to implement and extend key algorithms for learning and decision making (two programming projects).
- You will be able to identify an application of the theory in this course and formulate it mathematically (proposal).
- You will gain a deep understanding of an area of particular interest and apply it to a problem (final project).
- You will be able to critique approaches to solving decision problems (peer review). basic probability and fluency in a high-level programming language. This year we will be using the textbook titled *Algorithms for Decision Making*, MIT Press, 2022. Printed copies are available for purchase or you may download it for free as a PDF. Details are [here](https://algorithmsbook.com/decisionmaking).

The textbook serves as the official lecture notes.

AA 228 / CS 238 Decision Making under Uncertainty

Probability

4 courses cover this concept

CS 229: Machine Learning

CS 188 Introduction to Artificial Intelligence

15-381 Artificial Intelligence

AA 228 / CS 238 Decision Making under Uncertainty