1. copyright Paula Matuszek 2013 1 Kinds of Machine Learning

2. copyright Paula Matuszek 2013 2 Kinds of Machine Learning Machine learning techniques can be grouped into several categories, in several ways: What kind of input or data do they work on? What kind of output or result do they produce? What gets used as a critic? The typical broad categories are supervised learning unsupervised learning reinforcement learning

3. copyright Paula Matuszek 2013 3 Supervised Learning In supervised learning, we provide the system with example inputs and the result we want to see from those inputs. These are the training cases. each training case consists of a set of variables or features describing one case, including the decision that should be made the system builds a model from the examples and uses the model to make a decision the critic compares the actual decision to the desired decision and tweaks the model to make the actual and desired decisions more similar

4. copyright Paula Matuszek 2013 4 Supervised Learning Examples Learn to detect spam from example spam and non-spam email Decide whether a house will sell from a list of its features Decide the age and gender of a skeleton Need more!!

5. copyright Paula Matuszek 2013 5 Supervised Learning, continued Most commonly used machine learning methods are based on supervised learning. The success of a supervised learning system depends very heavily on the examples it is given. They must be typical or representative. It also depends on the data or features provided, the feature space. The feature space must reflect the domain or field.

6. copyright Paula Matuszek 2013 6 Unsupervised Learning The goal in unsupervised learning is often focused more on discovery than on specific decisions. Some examples: do my search results have some natural grouping? (eg, “bank” should give results related to finance and results related to rivers) can I identify categories or genres of books based on what people purchase? Based on their vocabulary? NEED MORE FROM DIFFERENT DOMAINS

7. copyright Paula Matuszek 2013 7 Unsupervised Learning In an unsupervised learning application, we do not give the system any a priori decisions. The task instead is to find similarities among the examples given and group them The critic is some way of measuring the similarity among the cases, such as shared vocabulary. The data we provide define the kind of similarities and groupings we will find. Important to have representative examples and relevant features.

8. copyright Paula Matuszek 2013 8 Supervised or Unsupervised? Sort news articles into Government, Sports, Science, Technology based on a set of labeled articles from the last five years. Sort today’s news articles from 100 different news sources to see which topics are being discussed

9. copyright Paula Matuszek 2013 9 Reinforcement Learning Reinforcement learning systems learn a series of actions or decisions, rather than a single decision, based on feedback given at the end of the series. For instance, the Animals game makes multiple moves, but the critic gives only whether the game was won or lost. A reinforcement learner has a goal, and carries out trial-and-error search to find the best paths toward that goal

10. copyright Paula Matuszek 2013 10 Reinforcement Learning A typical reinforcement learning system is an active agent, interacting with its environment. It must balance exploration: trying different actions and sequences of actions to discover which ones work best achievement: using sequences which have worked well so far It must also learn successful sequences of actions in an uncertain environment Typical current applications are in artificial intelligence and in engineering. Can we come up with some good domain examples??

11. copyright Paula Matuszek 2013 11 Some Additional Machine Learning Methods Case-Based Reasoning: The system has a set of cases with outcomes and a set of rules for adapting outcomes ; new cases are resolved by picking similar cases and adapting them, then added to the representation. Self-Organizing Maps: (Having trouble phrasing this one in English...) Dimensionality Reduction: Genetic Algorithms (I’m still working on what goes on this slide and how to phrase it)