Understanding Artificial Intelligence through Algorithmic Information Theory

Understanding Artificial Intelligence through Algorithmic Information Theory

Course
en
English
30 h
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  • Self-paced
  • Free Access
  • Fee-based Certificate
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  • 5 Sequences
  • Advanced Level

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Course details

Syllabus

Chapter 1. Describing data

  • Complexity as code length
  • Conditional Complexity

Chapter 2. Measuring Information

  • Complexity and frequency
  • Meaning distance

Chapter 3. Algorithmic information & mathematics

  • Algorithmic probability, Randomness
  • Gödel’s theorem

Chapter 4. Machine Learning and Algorithmic Information

  • Universal induction - MDL
  • Analogy & Machine Learning as complexity minimization

Chapter 5. Subjective information

  • Simplicity & coincidences
  • Subjective probability
  • Relevance

Prerequisite

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.

Instructors

Jean-Louis Dessalles
Associate Professor • Telecom Paris - Institut Polytechnique de Paris

Editor

The Institut Mines-Télécom is a major public player in higher education, research and innovation in the fields of engineering and management.

It is made up of 10 Mines and Télécom grandes écoles under the supervision of the Minister for Productive Recovery (Mines Albi, Mines Alès, Mines Douai, Mines Nantes, Mines ParisTech, Mines Saint-Etienne, Télécom Bretagne, Télécom École de Management, Télécom ParisTech, Télécom SudParis) and two subsidiary schools (Télécom Lille and Eurecom). It has a special relationship with two strategic partners, Mines Nancy, part of the University of Lorraine, and Armines.

Institut Mines-Télécom is at the forefront of educational innovation. It is the leading group of engineering schools in France, graduating 8% of engineers each year.

Its open online courses are part of a movement to create and lead communities for initial and lifelong learning. Institut Mines-Télécom will contribute to this movement and will involve all of its stakeholders: teacher-researchers, students and future students, graduates, companies and, more generally, all learners interested in the Institute's areas of specialisation.

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