The goal of Artificial Intelligence (AI) is to tackle complex real-world problems with rigorous mathematical tools. Common sub-topics include Machine Learning, Search, Markov Decision Processes, Reinforcement Learning, etc.
Courses of Artificial Intelligence usually requires knowledge of Linear Algebra, College Calculus, Probability and Statistics, and proficiency of Computer Programming, preferably Python. Some course may recommend familiarity of Machine Learning
Brown University
Fall 2022
CS1410 at Brown University delves into the realm of Artificial Intelligence. Using the 3rd edition of "Artificial Intelligence, A Modern Approach" by Russell & Norvig, students explore intelligent agents, game theory, knowledge representation, logic, probabilistic learning, NLP, robotics, computer vision, and ethical implications of AI.
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.
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.
Stanford University
Winter 2023
This course provides a deeper understanding of robot autonomy principles, focusing on learning new skills and physical interaction with the environment and humans. It requires familiarity with programming, ROS, and basic robot autonomy techniques.
Stanford University
Winter 2023
This course offers a solid introduction to the field of reinforcement learning (RL), covering challenges, approaches, and deep RL. Prerequisites include Python proficiency and foundations of machine learning. Students will be able to implement RL algorithms and evaluate them.