Machine learning the basics of machine learning, Slides of Machine Design

Download Machine learning the basics of machine learning and more Slides Machine Design in PDF only on Docsity! 1 Machine Learning: Lecture 1 Overview of Machine Learning (Based on Chapter 1 of Mitchell T.., Machine Learning, 1997) 2 Machine Learning: A Definition Definition: 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. 5 The amount of knowledge available about certain tasks might be too large for explicit encoding by humans (e.g., medical diagnostic).  Environments change over time.  New knowledge about tasks is constantly being discovered by humans. It may be difficult to continuously re-design systems “by hand”. Why is Machine Learning Important (Cont’d)? 6 Areas of Influence for Machine Learning Statistics: How best to use samples drawn from unknown probability distributions to help decide from which distribution some new sample is drawn?  Brain Models: Non-linear elements with weighted inputs (Artificial Neural Networks) have been suggested as simple models of biological neurons.  Adaptive Control Theory: How to deal with controlling a process having unknown parameters that must be estimated during operation? 7 Areas of Influence for Machine Learning (Cont’d) Psychology: How to model human performance on various learning tasks?  Artificial Intelligence: How to write algorithms to acquire the knowledge humans are able to acquire, at least, as well as humans?  Evolutionary Models: How to model certain aspects of biological evolution to improve the performance of computer programs? 10 2. Choosing the Training Experience Direct versus Indirect Experience [Indirect Experience gives rise to the credit assignment problem and is thus more difficult]  Teacher versus Learner Controlled Experience [the teacher might provide training examples; the learner might suggest interesting examples and ask the teacher for their outcome; or the learner can be completely on its own with no access to correct outcomes]  How Representative is the Experience? [Is the training experience representative of the task the system will actually have to solve? It is best if it is, but such a situation cannot systematically be achieved] 11 3. Choosing the Target Function Given a set of legal moves, we want to learn how to choose the best move [since the best move is not necessarily known, this is an optimization problem]  ChooseMove: B --> M is called a Target Function [ChooseMove, however, is difficult to learn. An easier and related target function to learn is V: B --> R, which assigns a numerical score to each board. The better the board, the higher the score.]  Operational versus Non-Operational Description of a Target Function [An operational description must be given]  Function Approximation [The actual function can often not be learned and must be approximated] 12 4. Choosing a Representation for the Target Function  Expressiveness versus Training set size [The more expressive the representation of the target function, the closer to the “truth” we can get. However, the more expressive the representation, the more training examples are necessary to choose among the large number of “representable” possibilities.]  Example of a representation:  x1/x2 = # of black/red pieces on the board  x3/x4 = # of black/red king on the board  x5/x6 = # of black/red pieces threatened by red/black V(b) = w0+w1.x1+w2.x2+w3.x3+w4.x4+w5.x5+w6.x6 wi’s are adjustable or “learnable” coefficients ^