Note: this post meant to help clarify the tutorial questions (number 1, 3, 4, 5) for COMP 9417 – Week 11, School of Computer Science and Engineering, UNSW (s1 – 2017)
Regardless of machine learning algorithm that we choose, we still may want to know about these stuffs (slide – page 6):
- How many training examples a learner should have before it converges to correct hypothesis? (sample complexity)
- How large is a hypothesis space? How complex is a learner’s hypothesis? (hypothesis complexity)
- How many errors a learner are allowed to misclassify before it finally converges to a successful hypothesis? (mistake bounds)
Let’s take a look at first aspect. Continue reading
The solutions for these notebooks: “Getting Started with Python and Regression” and “Perceptron training for linear classification” can be downloaded here.
Recently, there is a hype about smart robots that will replace humans in many areas. These robots usually has a certain degrees of artificial intelligence. But are most of the robots are truly intelligent? What is intelligence anyway? This post want to give simple explanation about intelligent robot from artificial intelligence (AI) perspective.
An example of “an intelligent robot”
Consider the example of a robot which competes in firefighting robot contest. In this competition, robot should explore a maze, with many obstacles, to find the fire and extinguish it. Although the robot might seem intelligence, but it is very limited. For instance, how if the source of fire is not inside the maze, but outside of it? How if the robot does not see the fire, but it sees a lot of smokes? Suddenly, this robot seems not so smart anymore when its environment, or its task, is changed.