Udacity-Machine Learning纳米学位－学习笔记1

课程地址

Category:

Machine Learning
   Artificial Intelligence
   Data Science
   Basic Statics

Tips

• stick to your schedule
• be relentless in searching the answer on your own
• be an active member in the community

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16/5/20

Welcome

Machine Learning

What is Machine Learning ?

• Processing the data & understanding the data
• Then react intelligently to it
• Build models to represent Data
• Lots of different things, really next natural evolution

Compare with traditional programming ?

• Traditional: Build the decision making directly into the programming
• ML: Build an agent who can look at a bunch of images over time and recognize

Application ?

• Almost every field : Predict, Identify, Maximize

Objectives ?

• When to use them, How
• What to apply to solve it, How to evaluate

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16/5/21

Artificial Intelligence & Data Science

2 Fields:

• Artificial Intelligence
• Data Science

Artificial Intelligence

• To create machines that are as smart as humans
• 6 Characteristics
• 5 Big problems to solve
• 4 Schools of AI
• 3 Fundamental Process of knowledge based AI
• Fundamental Tech: Bayesian Rule, Bayesian Network

Data Science

What is Data Scientist ?

• Can do math, and programming.
• Ask the right questions and solve them.
• Communicate, Report, and Present.

What does Data Scientist Do ?

• Data
• Model
• Understand patterns

Machine Learning

3 Parts:

• Supervised Learning:
  Labeled Data to get the label for new data
• Unsupervised Learning:
  Input->Observe the relationship among them->Identify
• Reinforcement Learning:
  **Learn from delayed award **

What to learn:

• Parameters
• Structure
• Hidden concepts

What for:

• Predict
• Diagnose
• Summarization

Output:

• Classification
• Regression

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16/5/22

Basic Statics Concepts

Basic Statics

Measure of Central Tendency

• mode, median, average

Variability of Data

• Range= Max-Min
• Quartile: Q1, IQR=Q3-Q1
• Outlier: IQR or >Q3+1.5IQR
• Variance: average( sum( (Xi-Xbar)^2 ) )
• Standard Deviation: squared root of Variance