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Lecture 4
- 3. Machine Learning 11-Oct-17 The problems of our human limitations go away if we can make computers do the dirty things for us. This is why machine learning is so popular. Humans can: think, learn, see, understand language, reason, etc. Artificial Intelligence aims to reproduce these capabilities. Machine Learning is one part of Artificial Intelligence. 3
- 4. What is Learning? 11-Oct-17 Learning is any process by which a system improves performance from experience. Humans and other animals can display behaviors that we label as intelligent by learning from experience. Learning a set of new facts Learning how to do something Improving ability of something already learned 4
- 5. Ways humans learn things 11-Oct-17 Talking, Walking, Running (Learning by mimicking, reading or being told facts) Tutoring (Being informed when one is correct) Experience (Feedback from the environment) Analogy (Comparing certain features of existing knowledge to new problems) Self-reflection (Thinking things in ones own mind, deduction, discovery) 5
- 6. Important parts of Learning 11-Oct-17 Remembering: Recognizing that last time we were in this situation, we tried out some particular action, and it worked. Adapting: So, we will try it again, or it didn’t work, so we will try something different. Generalizing: Recognizing similarity between different situations, so that things that applied in one place can be used in another. 6
- 7. Machine Learning 11-Oct-17 Ever since computers were invented, we have wondered whether they might be made to learn. The ability of a program to learn from experience — that is, to modify its execution on the basis of newly acquired information. Machine learning is about automatically extracting relevant information from data and applying it to analyze new data. 7
- 8. Machine Learning (Definition) 11-Oct-17 A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Arthur Samuel (1959). Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed. Optimize a performance criterion using example data or past experience. 8
- 9. What is the Learning Problem? 11-Oct-17 Learning = Improving with experience at some task Improve over task T With respect to performance measure P Based on experience E 9
- 11. Types of Machine Learning 11-Oct-17 Supervised learning: Training data includes desired outputs. Based on this training set, the algorithm generalizes to respond correctly to all possible inputs. Unsupervised learning: Training data does not include desired outputs, instead the algorithm tries to identify similarities between the inputs that have something in common are categorized together. 11
- 12. Types of Machine Learning 11-Oct-17 Reinforcement learning: (Rewards from policy) Reinforcement learning is learning what to do--how to map situations to actions--so as to maximize a numerical reward signal. The agent acts on its environment, it receives some evaluation of its action (reinforcement), but is not told of which action is the correct one to achieve its goal; but instead The learner must discover which actions yield the most reward by trying them. Evolutionary learning: Biological organisms adapt to improve their survival rates and chance of having offspring in their environment, using an idea of fitness (how good the current solution is). This type of learning is inspired by biological evolution such as reproduction, mutation, recombination, natural selection and survival of the fittest. 12
- 16. Regression 11-Oct-17 A technique for determining the statistical relationship between two or more variables where a change in a dependent variable is associated with, and depends on, a change in one or more independent variables. 16