Contraint Satisfaction Problems

Informed Search

Adversarial Search

Linear Programming

Integer Programming

Propositional calculus (Propositional logic)

First-order logic

Reinforcement learning (RL)

Bayes' Nets: Independence

Bayes' Nets: Representation

Hidden Markov Model (HMM)

Particle Filtering

Game Theory: Equilibrium

Probability

Agents and Search

Knowledge representation and reasoning

Classical Planning

Particle Filter

Markov Decision Process (MDP)

Bayes' Nets: Inference

Game Theory: Social Choice

Logical Agents

Bayes' Nets: Sampling

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

Artificial Intelligence

Constraint satisfaction problem

Contraint Satisfaction Problems

1 courses cover this concept

15-381 Artificial Intelligence