3. What is Machine Learning?
Arthur Samuel (1959).
Machine Learning: Field of study that gives
computers the ability to learn without being
explicitly programmed.
Tom Mitchell (1998)
A computer program is said to learn from
experience E with respect to some task T and
some performance measure P, if its
performance on T, as measured by P,
improves with experience E.
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4. What is Machine Learning?
Examples:
-Database mining Large datasets from growth of
automation/web.
E.g., Web click data, medical records, biology,
engineering
-Applications can’t program by hand.
E.g., Autonomous helicopter, handwriting
recognition, most of Natural Language Processing
(NLP), Computer Vision.
-Self-customizing programs
E.g., Amazon, Netflix product recommendations
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5. Machine learning algorithms
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-Supervised learning
In supervised learning, we already know what our
correct output should look like, having the idea
that there is a relationship between the input and
the output.
-Unsupervised learning
Unsupervised learning allows us to approach
problems with little or no idea what our results
should look like. We can drive structure from data
where we don’t necessarily know the effect of the
variables.
8. Unsupervised learning
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We can drive structure by clustering the data
based on relationships among the variables in
the data.
어느 것이 맞는 답인지 알려줌. Data set이 주어지지만 어떤 것인지, 무엇
알려주지 않음.
11. Unsupervised learning
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Non-clustering
The “Cocktail Party Algorithm”, allows you to find
structure in a chaotic environment.
(i.e. identifying individual voices and music from
a mesh of sounds at a cocktail party)
15. Model and Cost Function
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Cost Function : We can measure the accuracy
of our hypothesis function by using a cost
function. This takes an average difference
(actually a fancier version of an average) of all
the results of the hypothesis with inputs from
x's and the actual output y's.
비용함수를 사용하면 주어진 데이터에 가장 가
까운 일차함수를 구할 수 있다.
개수
17. Model and Cost Function
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Predicted value와 actual value의 차의 제곱의
평균을 구함
X에 대한 함수 h, parameter theta에 대한 함수
J.
Cost function J를 최소화하는 theta를 찾는 것
이 목표.
22. Parameter Learning
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이 기호, := 는 할당받
는 것을 의미
α가 의미하는 바는
기본적으로
우리가 언덕을 내려
가기 위해 얼만큼 큰
걸음을 내딛어야하
는가 에 대한것
미분계수
값을 빼가면서 최적의 값을 도출.
주의! 계산할 때, theta0과 theta1은 값을
동시에 바꿔서 대입해야 한다.