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
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
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
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: <Q1-1.5IQR or >Q3+1.5IQR
- Variance: average( sum( (Xi-Xbar)^2 ) )
- Standard Deviation: squared root of Variance
