# Understanding Artificial Intelligence through Algorithmic Information Theory

*Can we characterize intelligent behavior?*

*Are there theoretical foundations on which Artificial Intelligence can be grounded?*

**This course on Algorithmic Information will offer you such a theoretical framework.**

- You will be able to see machine learning, reasoning, mathematics, and even human intelligence as abstract computations aiming at compressing information.
- This new power of yours will not only help you understand what AI does (or can’t do!) but also serve as a guide to design AI systems.

### There is one session available:

After a course session ends, it will be archived.

Starts Oct 27

Ends Dec 31

Estimated 5 weeks

4–8 hours per week

Self-paced

Progress at your own speed

Free

Optional upgrade available

##### Understanding Artificial Intelligence through Algorithmic Information Theory

### At a glance

- Institution: IMTx
- Subject: Computer Science
- Level: Advanced
- Prerequisites:
*Examples of what you should know before embarking on this course*- what a convex curve looks like,
- that log(7^n) is
*n*times log(7) - that rational numbers have finite or periodic expansion,
- that rational numbers are countable, but that real numbers are not,
- that the probability of "A and B" is the probability of "A knowing B" times the probability of B,
- that 65 is 1000001 is in base 2 and 41 in base 16,
- how to compute the sum of a finite geometric series,
- that {'a':1, 'i':0} is a Python dictionary and why list('ab'*4)[::2] yields ['a','a','a','a'],
- that k-means is a clustering method,
- what Bayes’ theorem tells us,
- how Shannon’s information is related to probability,
- that what is called a
*Turing machine*is NOT the machine that Alan Turing (Benedict Cumberbatch)

is using in the movie*The imitation game*.

- Language: English
- Video Transcript: English

# About the instructors

# Who can take this course?

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