1. 1 Four Components of a Learning System t Performance system  S olve the given performance task  U se the learned target function  N ew problem -> trace of its solution t Critic  O utput a set of training examples of the target function

2. 2 Four Components of a Learning System (2) t Generalizer  Input: training example  Output: hypothesis (estimate of the target function)  Generalizes from the specific training examples  Hypothesizes a general function t Experiment generator  Input - current hypothesis  Output - a new problem  Picks new practice problem maximizing the learning rate

3. 3 Sequence of Design Choices

4. 4 Alternative Algorithms for Checkers Learning t Nearest neighbor algorithms (Chap. 8) t Genetic algorithms (Chap. 9) t Explanation-based learning (Chap. 11)

5. 5 1.3 Perspectives and Issues in ML t “Learning as search in a space of possible hypotheses” t Representations for hypotheses  Linear functions  Logical descriptions  Decision trees  Neural networks t Learning methods are characterized by their search strategies and by the underlying structure of the search spaces.

6. 6 Issues in Machine Learning t Algorithm for learning general target function from specific training examples t Amount of data t Helpful prior knowledge t Choice of strategy for next training experience t Method of reducing the learning task to function approximation t Automatically alter its representation for target function

7. 7 Questions about Machine Learning t What algorithms exist for learning general target functions from specific examples?  In what settings will particular algorithms converge to the desired function, given sufficient training data?  Which algorithms perform best for which types of problems and representations? t How much training data is sufficient?  What general bound can be found to relate the confidence in learned hypotheses to the amount of training experience and the character of the learner’s hypothesis space?

8. 8 Questions (2) t When and how can prior knowledge held by the learner guide the process of generalizing from examples?  Can prior knowledge be helpful even when it is only approximately correct? t What is the best strategy for choosing a useful next training experience, and how does the choice of this strategy alter the complexity of the learning problem?

9. 9 Questions (3) t What is the best way to reduce the learning task to one or more function approximation problems?  What specific functions should the system attempt to learn?  Can this process itself be automated? t How can the learner automatically alter its representation to improve its ability to represent and learn the target function?

10. 10 요 약요 약 t 기계학습은 다양한 응용분야에서 실용적 가치가 크다.  많은 데이터로부터 규칙성을 발견하는 문제 (data mining)  문제의 성격 규명이 어려워 효과적인 알고리즘을 개발할 지식이 없는 문제 영역 (human face recognition)  변화하는 환경에 동적으로 적응하여야 하는 문제 영역 (manufacturing process control) t 기계학습은 다양한 다른 학문 분야와 밀접히 관련 된다.  인공지능, 확률통계, 정보이론, 계산이론, 심리학, 신경과 학, 제어이론, 철학 t 잘 정의된 학습 문제는 다음을 요구한다.  문제 (task) 의 명확한 기술, 성능평가 기준, 훈련경험을 위 한 사례

11. 11 요약 ( 계속 ) t 기계학습 시스템의 설계 시에는 다음 사항을 고려 하여야 한다.  훈련경험의 유형 선택  학습할 목표함수  목표함수에 대한 표현  훈련 예로부터 목표함수를 학습하기 위한 알고리즘 t 학습은 가능한 가설 공간에서 주어진 훈련 예와 다 른 배경지식을 가장 잘 반영하는 하나의 가설을 탐 색하는 탐색이다.  다양한 학습 방법은 서로 다른 가설공간의 형태와 이 공 간 내에서 탐색을 수행하는 전략에 의해 규정 지어진다.