1. George Yauneridge

2.  Machine learning basics  Types of learning algorithms  Genetic algorithm basics  Applications and the future of genetic algorithms

3.  Machine learning is a topic of artificial intelligence  The focus is on developing and implementing algorithms that allow machines to “learn”  For machines, learning is acquiring new data and forming decisions based on all available data

4.  Case Based Reasoning  Modifies the solution from a past problem  Decision Trees  Analyze a tree of conditions to give yes/no answers  Data Mining and Pattern Recognition  Finds patterns in large amounts of data

5.  Neural Networks  Made of many units, each capable of input/output  Reinforcement Learning  The system analyzes its interactions with its environment, usually involving trial and error  Inductive Logic  Imitates human interpretation of data

6.  Use DNA and evolution from biology as a model  Use a population of parent solutions that compete to produce children  Only the strongest solutions pass on their information

7.  A each solution is called a chromosome  Carries the information  Several ways to encode the data  Examples C1: 011001110101 C2: 011010110100

8.  “Strong” chromosomes are selected to pass on their information  Strength of a chromosome is determined by a fitness function  Imitates Darwin’s survival of the fittest theory

9.  Chromosomes switch parts of their information to form children  Several different methods C1: 011001110101 C2: 011010110100 Crossover C1: 01100111o100 C2: 011010110101

10.  Certain bits are inverted  Imitates mutations that would occur in nature C1: 011001110101 Mutation C1: 010001110111

11.  Create a random population of chromosomes  Evaluate the fitness of each chromosome  Create the next population  Select 2 fit parents  Perform crossover  Perform mutation  Place the children in the new population  Using the new population, test if the end condition is met  Loop

12. http://www.obitko.com/tutorials/genetic-algorithms/example-function- minimum.php

13.  Evolving music  Strategy planning  Evolving programs  Other evolutionary and decision based applications

14. Association for the Advancement of Artificial Intelligence. http://aaai.org AI Horizon. http://aihorizon.com Marek Obitko. http://www.obitko.com/tutorials/genetic- algorithms CMU CS Department. http://www.cs.cmu.edu/afs/cs/project/ai- repository/ai/html/faqs/ai/genetic