Machine learning
15
Machine Learning
description
power point for ECC4301 - introduction on machine learning
Transcript of Machine learning
Machine Learning
Machine Learning
Definition:The ability of a machine to improve its performance
based on previous results.
Components to be learned
Direct-mapping from conditions on the current state to actions Means to infer properties from the percept sequence Information about the way the world evolves Utility information on desirability of world states Action information Goals describe the maximum achievement
Machine Learning methods
• Symbol-based – symbols represent entities & relationships
• Connectionist – patterns of activity in networks
• Genetic – Imitation of genetic & evolutionary process
• Stochastic – Insight that support Bayes' rule
Exercise
How to locate the license plate area and recognize the license plate info?
Why machine learning?• Recent progress in algorithms & theory• Growing flood of online data• Computational power is available• Budding industry
Three niches on machine learning• Data mining: using historical data to improve decision• Software applications we can't program by hand– Autonomous driving– Speech recognition
• Self customizing programs- Newsreader that learns user interests
General steps in ML
Problem statement
Featureextraction
ML implementation
Performance evaluation
Example problem: movie critics
Example method: K-means clustering algorithm
Group discussionGroup 1 – amira, meng kwang, nogol, musobi, hadiProblem: ML for recycling centre – paper, glass, plastic
Group discussionGroup 2 – airil, bryan,afsaneh2,farzaneh, azleenProblem: ML for junk email filter
Group discussion
Group 3 – malina, syaza, laith, afsaneh1, mehrdadProblem: ML for electronics gadget recommendation system ( can be specific – smartphone or tablet)
Group discussion – expected discussion
1. Explanation on problem statement2. List of appropriate features3. Chosen features (significant)4. ML Method5. Expected results
Class discussion
1. How do algorithms make recommendations from data?2. Why are features important?3. Would K-means work the same with more than 2 features?4. Could we visualize more than 2 features? More than 3?5. Think of how Euclidean Distance is calculated. Do all the features need to be on the same scale?6. What are challenges in solving your problem?
