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Machine Evolution: Foundations of AI

This course will cover an introduction to Genetic Algorithms, Fitness functions, Reproduction and mutation schemes.

  • Duration

    4 weeks

  • Weekly study

    0 hours
Overview
Requirements
Educators

Learn about Genetic Algorithms, Fitness functions and mutation schemes.

This course will cover an introduction to Genetic Algorithms, Fitness functions, Reproduction and mutation schemes. Learn about machine learning with neural networks.

You will contrast them with other types of prediction machines and introduce the single layer neural network and explain its limitations. You will also work with the Ancient Brain platform.

What topics will you cover?

  • Topic 1: Machine Evolution. We will introduce the concept of evolving a program or a machine. We will list the concepts (like reproduction) that need to be precisely defined in order to get an algorithm. Week 1 will demo a Machine Evolution “world” on our coding site Ancient Brain, but we will not understand the code yet.
  • Topic 2: Machine Evolution - Implementation. We consider the various ways of defining the “DNA” of an individual in machine evolution. We then consider defining “survival of the fittest” as a precise algorithm. There are many forms of picking the “fittest” and they lead to different forms of evolutionary search.
  • Topic 3: Machine Evolution - Implementation (more). We define reproduction. How different should a new individual be from their “parents”? We define crossover and mutation as algorithms. Different definitions lead to different forms of evolutionary search. In this week, we finally understand enough to understand the code of the Machine Evolution “world” on Ancient Brain, and students have an exercise to do with it.
  • Topic 4: Machine Evolution (finish). We consider applications of Machine Evolution. Another working program (“World”) on Ancient Brain is provided, a machine evolution solution to the Traveling Salesman Problem. This has another coding exercise.

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Demonstrate a thorough understanding of how to evolve solutions for different problems that are amenable to a machine evolution approach.
  • Demonstrate an understanding of different forms of genotype encodings, fitness functions, crossover and mutation.
  • Demonstrate the ability to code a machine evolution algorithm for two different problems, using our online coding platform.

Who is the course for?

The course is aimed at IT professionals in employment in Republic of Ireland registered companies. To qualify for direct entry they must have a Level 8 Honours Degree (2.2) or higher in Computer Science, Computing, Computer Applications or a related discipline. Applicants without these entry requirements (e.g., Level 7 degree or lower than an Honours 2.2 in a Level 8 degree) may be considered if they can demonstrate previously obtained competence equivalent to the entry requirements.

Who will you learn with?

Mark Humphrys

Dr. Mark Humphrys is a lecturer at DCU. He has a BSc from UCD and a PhD from Cambridge. His research interests are in AI. He is the inventor of the coding site "Ancient Brain".

Who developed the course?

Dublin City University

Dublin City University is a young, dynamic and ambitious Irish university with a distinctive mission to transform lives and societies through education, research and innovation.

Endorsers and supporters

funded by

Skillnet Ireland

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