Artificial Intelligence (AI) (edX)

Artificial Intelligence (AI) (edX)
Course Auditing
Categories
Effort
Certification
Languages
Students are required to have some basic of Python programming and an understanding of probability. Homework assignments will have a programming component in Python.
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Artificial Intelligence (AI) (edX)
Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems. 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 intelligence (AI).

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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.

You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction 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.

This course is part pf the Artificial Intelligence MicroMasters Program.


What you'll learn:

- Introduction to Artificial Intelligence and intelligent agents, history of Artificial Intelligence

- Building intelligent agents (search, games, logic, constraint satisfaction problems)

- Machine Learning algorithms

- Applications of AI (Natural Language Processing, Robotics/Vision)

- Solving real AI problems through programming with Python


Course Syllabus


Week 1: Introduction to AI, history of AI, course logistics

Week 2: Intelligent agents, uninformed search

Week 3: Heuristic search, A* algorithm

Week 4: Adversarial search, games

Week 5: Constraint Satisfaction Problems

Week 6: Machine Learning: Basic concepts, linear models, perceptron, K nearest neighbors

Week 7: Machine Learning: advanced models, neural networks, SVMs, decision trees and unsupervised learning

Week 8: Markov decision processes and reinforcement learning

Week 9: Logical Agent, propositional logic and first order logic

Week 10: AI applications (NLP)

Week 11: AI applications (Vision/Robotics)

Week 12: Review and Conclusion


Prerequisites

Students are required to have some basic of Python programming and an understanding of probability. Homework assignments will have a programming component in Python. The course offers an excellent opportunity for students to dive into Python while solving AI problems and learning its applications.

- Linear algebra (vectors, matrices, derivatives)

- Calculus

- Basic probability theory

- Python programming



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Course Auditing
206.00 EUR
Students are required to have some basic of Python programming and an understanding of probability. Homework assignments will have a programming component in Python.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.