2_1. Types of Machine Learning, History of ML.pdf
- 2. Types of Learning • Supervised (inductive) learning – Given: training data + desired outputs (labels) • Unsupervised learning – Given: training data (without desired outputs) • Semi-supervised learning – Given: training data + a few desired outputs • Reinforcement learning – Rewards from sequence of actions
- 7. Supervised Learning: Regression • Given (x1, y1), (x2, y2), ..., (xn, yn) • Learn a function f(x) to predict y given x – y is real-valued == regression 0 1 2 3 4 5 6 7 8 9 1970 1980 1990 2000 2010 2020 September Arctic Sea Ice Extent (1,000,000 sq km) Year
- 11. Supervised Learning: Classification • Given (x1, y1), (x2, y2), ..., (xn, yn) • Learn a function f(x) to predict y given x – y is categorical == classification 1(Malignant) 0(Benign) Tumor Size Breast Cancer (Malignant / Benign)
- 12. Supervised Learning: Classification • Given (x1, y1), (x2, y2), ..., (xn, yn) • Learn a function f(x) to predict y given x – y is categorical == classification 1(Malignant) 0(Benign) Tumor Size Breast Cancer (Malignant / Benign) Tumor Size
- 13. Supervised Learning: Classification • Given (x1, y1), (x2, y2), ..., (xn, yn) • Learn a function f(x) to predict y given x – y is categorical == classification 1(Malignant) 0(Benign) Tumor Size Breast Cancer (Malignant / Benign) Tumor Size Predict Malignant Predict Benign
- 14. Supervised Learning Tumor Size Age - Clump Thickness - Uniformity of Cell Size - Uniformity of Cell Shape … • x can be multi-dimensional – Each dimension corresponds to an attribute
- 18. Unsupervised Learning • Given x1, x2, ..., xn (without labels) • Output hidden structure behind the x’s – E.g., clustering
- 19. Unsupervised Learning • Independent component analysis – separate a combined signal into its original sources
- 20. Designing a Learning System • Choose the training experience • Choose exactly what is to be learned – i.e. the target function • Choose how to represent the target function • Choose a learning algorithm to infer the target function from the experience Environment/ Experience Learner Knowledge Performance Element Training data Testing data
- 21. Training vs. Test Distribution • We generally assume that the training and test examples are independently drawn from the same overall distribution of data – We call this “i.i.d” which stands for “independent and identically distributed” • If examples are not independent, requires collective classification • If test distribution is different, requires transfer learning
- 22. Various Function Representations • Numerical functions – Linear regression – Neural networks – Support vector machines • Symbolic functions – Decision trees – Rules in propositional logic – Rules in first-order predicate logic • Instance-based functions – Nearest-neighbor – Case-based • Probabilistic Graphical Models – Naïve Bayes – Bayesian networks – Hidden-Markov Models (HMMs) – Probabilistic Context Free Grammars (PCFGs) – Markov networks
- 23. Various Search/Optimization Algorithms • Gradient descent – Perceptron – Backpropagation • Dynamic Programming – HMM Learning – PCFG Learning • Divide and Conquer – Decision tree induction – Rule learning • Evolutionary Computation – Genetic Algorithms (GAs) – Genetic Programming (GP) – Neuro-evolution
- 24. Evaluation • Accuracy • Precision and recall • Squared error • Likelihood • Posterior probability • Cost / Utility • Margin • Entropy • K-L divergence • etc.
- 25. A Brief History of Machine Learning
- 26. 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 – Michalski’s AQ and soybean diagnosis – Scientific discovery with BACON – Mathematical discovery with AM
- 27. 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 PAC Learning Theory – Focus on experimental methodology • 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
- 28. 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) – E-mail management – Personalized assistants that learn – Learning in robotics and vision • 2010s – Deep learning systems – Learning for big data – Bayesian methods – Multi-task & lifelong learning – Applications to vision, speech, social networks, learning to read, etc. – ???