[K-MOOC] Instruction to Deep Learning: 1-2. Methodology of Machine Learning

Machine Learning is so Extensive and Sophisticated

Machine Learning Tasks

1. Classification
1. to classify the data into specific category
2. categories are pre-assigned
   
2. Regression
1. Linear regression
1. to find linear function that explain independent variable x and dependent variable y in given data set, {(x, y)}
2. simple linear regression:
   y-hat = f(x) = 𝜷₀ + 𝜷₁𝑿𝑖
2. Logistic regression
1. is different to linear regression in that dependent variable(y) is nominal type
2. is a kind of stochastic elements
   
3. Clustreing
1. is almost the same to the classification, but the only one difference is that is has no certain categorues
2. to make cluster by learning characters of independent data (not need training)
3. application cases
1. to classify document: by frequency of words
2. to classify satellite pictures: by color tone

Machine Learning Modes

1. Supervised learning
1. teacher + students
2. classification, regression
2. Unsupervised learning
1. is trained with unlabeled data (without answer)
2. clustering
3. Reinforcement learning
1. was invented in 1990s, and is spotlighted in these days
2. is learning method that makes computer plays better than human being
3. to mapping the state to an specific action which brings the best reward

Machine Learning Technique