Tutorial breve de Machine LearningDescripción completa
Descripción: ENSAYO
Basic introduction to ML
machine learning
TensorFlow Machine Learning Cookbook
Machine Learning for Dummies
Book on the basics of Machine Learning
Guide
Some Machine Learning
Introduction to Machine Learning Research on Time Series.
Understanding Machine Learning
Slides about machine learning algorithms
Descripción: Understanding Machine Learning
Descripción: Real World Machine Learning
Understanding Machine LearningFull description
Descripción: TensorFlow Machine Learning Cookbook
Descrição: Book on the basics of Machine Learning
Pengertian Machine Learning 1
Ebook on machine learning basic concepts organized from wikipedia articlesFull description
Book on the basics 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
Performance evaluation
Feature extraction
ML implementation
Example problem: movie critics
Example method: K-means clustering algorithm
Group discussion Group 1 – amira, meng kwang, nogol, musobi, hadi Problem: ML for recycling centre – paper, glass, plastic
Group discussion Group 2 – airil, bryan,afsaneh2,farzaneh, azleen Problem: ML for junk email filter
Group discussion Group 3 – malina, syaza, laith, afsaneh1, mehrdad Problem: ML for electronics gadget recommendation system ( can be specific – smartphone or tablet)
Group discussion – expected discussion 1. Explanation on problem statement 2. List of appropriate features 3. Chosen features (significant) 4. ML Method 5. 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?
