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

Welcome
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
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
Almost every field : Predict, Identify, Maximize
When to use them, How
What to apply to solve it, How to evaluate

Artificial Intelligence & Data Science
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
Can do math, and programming.
Ask the right questions and solve them.
Communicate, Report, and Present.
Data
Model
Understand patterns

Machine Learning
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 **
Parameters
Structure
Hidden concepts
Predict
Diagnose
Summarization
Classification
Regression

Basic Statics Concepts
Basic Statics
mode, median, average
Range= Max-Min
Quartile: Q1, IQR=Q3-Q1
Outlier:
Variance: average( sum( (Xi-Xbar)^2 ) )
Standard Deviation: squared root of Variance
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