[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

[K-MOOC] Instruction to Deep Learning: Syllabus

• Course: Instruction to Deep Learning
• Professor: Hee-chul Kim / Daegu University
• Goals
• Understanding principle of deep learning and algorithm
• Prerequisite
• Mathematics(matrix, vector, differential), Computer
• Schedule
• 1 week: Understanding of machine learning
• 1-1. Outline of Machine Learning
• 1-2. Methodology of Machine Learning

[K-MOOC] Instruction to Deep Learning: 1-1. Outline of machine learning

Definition of Artificial Intelligence

1. the state that machine has intelligence (Nils Nilsson, 2010, 'The quest of AL')
2. but the problem is that the intelligence is ambiguous
   

Practical Definition of AI

1. the technology that machine could carry out a process in smart way
2. whole range of research of AI scientist (Stanford AI 100 years Report)
3. the complex of all elements of recognition process in human being
   

History of AI

1. 1956s: 
1. a first use of the term, 'Artificial Intelligence', 
2. meaning of 'to proceed on the basis of the conjecture that every of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it' (on Dartmouth College Workshop)
   
   
   
2. 1970s, 1990s: First AI winter, even though huge amount of investments and financial supports is gathered, the outcome was none and all those supports were cut off
   
3. 2010s: the interest and expectation to AI is growing
   
   

Relationship of AI, ML, DL

Definition of Machine Learning

1. the completely different method from the conventional programming method
1. programming method: data → program → output
2. Machine learning: data & output → algorithm → program
2. a detailed field of AI that functions intellectually after a computer is learning from its experience
3. in mathematical meaning,
1. y = h(x)
   y: output
   h: function
   x: feature
2. is finding the function h, h( ), which is the closest to target function by using a set of sample, S={(x, y)}
   

What is the Deep Learning?

1. a multilayered structure of Neural Networks (large scale)
2. is being trained by hierarchical abstract learning

Benefits of DL

1. End-to-End learning: just give input data and get output