What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common? They are all complex real world problems being solved with applications of artificial intelligence (AI). This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems. Hands on experience will be gained by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to machine learning includes work on linear regression.
Classics of edX, includes wide-ranging topics of AI and machine learning. Recommended for everyone who wants to start with artificial intelligence.
(It´s avaible as part of a 4-5 Weeks free trial of edX)
Introduction to AI, history of AI, course logistics
Intelligent agents, uninformed search
Heuristic search, A* algorithm
Adversarial search, games
Constraint Satisfaction Problems
Machine Learning: Basic concepts, linear models, perceptron, k-nearest neighbors algorithm
Machine Learning: advanced models, neural networks, SVMs (Support vector machines), decision trees and unsupervised learning
Markov decision processes and reinforcement learning
Logical Agent, propositional logic and first order logic
AI applications (Natural Language Processing)
AI applications (Vision/Robotics)
By the end of the course, you‘ll be able to...
- Introduction to Artificial Intelligence and intelligent agents, history of Artificial IntelligenceBuilding intelligent agents (search, games, logic, constraint satisfaction problems)
- Understanding machine learning algorithms
- Applications of AI (Natural Language Processing, Robotics/Vision)
- Solving real AI problems through programming with Python
- Linear algebra (vectors, matrices, derivatives)
- Basic probability theory
- Python programming