![[1] W. L. Chao, J. J. Ding, “Integrated Machine
Learning Algorithms for Human Age
Estimation”, NTU, 2011.
Reference
24](https://image.slidesharecdn.com/anoverviewofmachinelearning-170805125205/85/Simple-overview-of-machine-learning-24-320.jpg)
Simple overview of machine learning
- 1. MACHINE LEARNING Priyadharshini .R Mtech-(IT) Reg no:2016246017 1
- 2. Evolution of Machine Learning 2
- 3. What is machine learning? 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. Definition of Machine learning: Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. 3
- 4. History of Machine Learning • 1950s – Samuel’s checker player – Selfridge’s Pandemonium • 1960s: – Neural networks: Perceptron – Pattern recognition – Learning in the limit theory – Minsky and Papert prove limitations of Perceptron • 1970s: – Symbolic concept induction – Winston’s arch learner – Expert systems and the knowledge acquisition bottleneck – Quinlan’s ID3 4
- 5. History of Machine Learning (cont.) • 1980s: – Advanced decision tree and rule learning – Explanation-based Learning (EBL) – Learning and planning and problem solving – Utility problem – Analogy – Cognitive architectures – Resurgence of neural networks (connectionism, backpropagation) – Valiant’s Learning Theory – Focus on experimental methodology 5
- 6. 1990s: -Data mining -Adaptive software agents and web applications -Text learning -Reinforcement learning (RL) -Inductive Logic Programming (ILP) -Ensembles: Bagging, Boosting, and Stacking -Bayes Net learning History of Machine Learning (cont.) 6
- 7. History of Machine Learning (cont.) • 2000s – Support vector machines – Kernel methods – Graphical models – Statistical relational learning – Transfer learning – Sequence labeling – Collective classification and structured outputs – Computer Systems Applications • Compilers • Debugging • Graphics • Security (intrusion, virus, and worm detection) 7
- 8. Traditional Programming Machine Learning Computer Data Program Output Computer Data Output Program 8
- 9. Types of Learning • Supervised (inductive) learning – Training data includes desired outputs • Unsupervised learning – Training data does not include desired outputs • Semi-supervised learning – Training data includes a few desired outputs • Reinforcement learning – Rewards from sequence of actions 9
- 10. Many online software packages & datasets • onlineData sets • UC Respirtory • http://www.kdnuggets.com/datasets/index.html • Software (much related to data mining) • JMIR Open Source • Weka • Shogun • RapidMiner • ODM • Orange • CMU • Several researchers put their software online 10
- 11. Training and testing Training set (observed) Universal set (unobserved) Testing set (unobserved) Data acquisition Practical usage 11
- 12. 12 Learning (training): Learn a model using the training data Testing: Test the model using unseen test data to assess the model accuracy Training and testing
- 13. Optimization Combinatorial optimization – E.g.: Greedy search Convex optimization – E.g.: Gradient descent Constrained optimization – E.g.: Linear programming 13
- 14. 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 14
- 15. Supervised learning – Prediction – Classification (discrete labels), Regression (real values) Unsupervised learning – Clustering – Probability – estimation – Finding association (in features) – Dimension reduction Semi-supervised learning Reinforcement learning – Decision making (robot, chess machine) Algorithms 15
- 16. Algorithms Supervised learning Unsupervised learning Semi-supervised learning 16
- 17. 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 17
- 18. 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 18
- 19. Issues in Machine Learning • Problem representation / feature extraction • Intention/independent learning • Integrating learning with systems • What are the theoretical limits of learnability • Transfer learning • Continuous learning 19
- 20. Measuring Performance • Generalization accuracy • Solution correctness • Solution quality (length, efficiency) • Speed of performance 20
- 21. Working Applications of ML • Electrical power control • Chemical process control • Character recognition • Face recognition • DNA classification • Credit card fraud detection • Cancer cell detection 21
- 22. Current Machine Learning Research • Representation – data sequences – spatial/temporal data – probabilistic relational models – … • Approaches – ensemble methods – cost-sensitive learning – active learning – semi-supervised learning – collective classification – … 22
- 23. 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
- 24. [1] W. L. Chao, J. J. Ding, “Integrated Machine Learning Algorithms for Human Age Estimation”, NTU, 2011. Reference 24