An overview of machine learning (1)
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This document provides an overview of machine learning. It defines machine learning as using algorithms to allow computers to learn from empirical data. The document outlines different machine learning models, algorithms, and techniques including supervised learning, unsupervised learning, performance factors, and applications. It concludes that machine learning will become increasingly important and be applied to more solutions.
![[1] W. L. Chao, J. J. Ding, “Integrated
Machine Learning Algorithms for Human
Age Estimation”, NTU, 2011.
Reference](https://image.slidesharecdn.com/anoverviewofmachinelearning1-170212052852/85/An-overview-of-machine-learning-1-23-320.jpg)
An overview of machine learning (1)
- 1. An Overview of Machine Learning Speaker: Yi-Fan Chang Adviser: Prof. J. J. Ding Date: 2011/10/21
- 2. What is machine learning? Learning system model Training and testing Performance Algorithms Machine learning structure What are we seeking? Learning techniques Applications Conclusion Outline & Content
- 3. A branch of artificial intelligence, concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data. As intelligence requires knowledge, it is necessary for the computers to acquire knowledge. What is machine learning?
- 5. Training and testing Training set (observed) Universal set (unobserve d) Testing set (unobserve d) Data acquisition Practical usage
- 6. Training is the process of making the system able to learn. No free lunch rule: Training set and testing set come from the same distribution Need to make some assumptions or bias Training and testing
- 7. There are several factors affecting the performance: Types of training provided The form and extent of any initial background knowledge The type of feedback provided The learning algorithms used Two important factors: Modeling Optimization Performance
- 8. The success of machine learning system also depends on the algorithms. The algorithms control the search to find and build the knowledge structures. The learning algorithms should extract useful information from training examples. Algorithms
- 9. Supervised learning ( ) Prediction Classification (discrete labels), Regression (real values) Unsupervised learning ( ) Clustering Probability distribution estimation Finding association (in features) Dimension reduction Semi-supervised learning Reinforcement learning Decision making (robot, chess machine) Algorithms
- 11. Supervised learning Machine learning structure
- 12. Unsupervised learning Machine learning structure
- 13. Supervised: Low E-out or maximize probabilistic terms Unsupervised: Minimum quantization error, Minimum distance, MAP, MLE(maximum likelihood estimation) What are we seeking? E-in: for training set E-out: for testing set
- 14. Under-fitting VS. Over-fitting (fixed N) What are we seeking? error (model = hypothesis + loss functions)
- 15. Supervised learning categories and techniques Linear classifier (numerical functions) Parametric (Probabilistic functions) Naïve Bayes, Gaussian discriminant analysis (GDA), Hidden Markov models (HMM), Probabilistic graphical models Non-parametric (Instance-based functions) K-nearest neighbors, Kernel regression, Kernel density estimation, Local regression Non-metric (Symbolic functions) Classification and regression tree (CART), decision tree Aggregation Bagging (bootstrap + aggregation), Adaboost, Random forest Learning techniques
- 16. Techniques: Perceptron Logistic regression Support vector machine (SVM) Ada-line Multi-layer perceptron (MLP) Learning techniques , where w is an d-dim vector (learned) • Linear classifier
- 17. Learning techniques Using perceptron learning algorithm(PLA) Trainin g Testing Error rate: 0.10 Error rate: 0.156
- 18. Learning techniques Using logistic regression Trainin g Testing Error rate: 0.11 Error rate: 0.145
- 19. Support vector machine (SVM): Linear to nonlinear: Feature transform and kernel function Learning techniques • Non-linear case
- 20. Unsupervised learning categories and techniques Clustering K-means clustering Spectral clustering Density Estimation Gaussian mixture model (GMM) Graphical models Dimensionality reduction Principal component analysis (PCA) Factor analysis Learning techniques
- 21. Face detection Object detection and recognition Image segmentation Multimedia event detection Economical and commercial usage Applications
- 22. We have a simple overview of some techniques and algorithms in machine learning. Furthermore, there are more and more techniques apply machine learning as a solution. In the future, machine learning will play an important role in our daily life. Conclusion
- 23. [1] W. L. Chao, J. J. Ding, “Integrated Machine Learning Algorithms for Human Age Estimation”, NTU, 2011. Reference