📗 -> 12/03/24: COURSE QUICK REVIEW
[Lecture Slide Link]
🎤 Vocab
❗ Unit and Larger Context
Small summary
✒️ -> Scratch Notes
Ch.2
Conditional prob, bayes theorem
Bayes Method(ML)
- P(Class | Feature) = P(Feature|Class)P(Class) / P(Feature)
Ch. 3
E(X) = Long run fraction of outcomes. Mean.
Ch. 4
Families of Discrete RVs
- Uniform
- Geometric (EX = 1/p)
- Indicator (EX=p)
- Binomial
- Poisson
Ch. 7
Continuous RVs
PDF:
Continuous vs Discrete expectation
Families
- Uniform, Exp., normal
Ch. 11
Normal Dist
CLT
Total of same dist
Z-Transformation
Sample Average
is sample variance
- How does xbar vary?
- It varies by the var of population over n
Ch. 12-13
Confidence Interval
Hypothesis Testing
Useful when you have a known or asserted statistic
- IE testing whether smokers and non smokers have the same rate of cancer (diff=0)
Sample stat is telling you different story against sample
Markov Chains
Hot Dogs vs Hambugers
| HD | HB | |
|---|---|---|
| HD | .8 | .2 |
| HB | .4 | .6 |
| Property depends on that transitions only dependent on the last state, and not any previous ones (memoryless) |
K-Means
Randomly Place Center
- Calculatate btw each Home and Center A, Center B
- Place homes in the center they are closer to
- Replace center and repeat
Linear Regression
Goal is to minimize the error from prediction to actual
y is dependent on x, predict y based on x
(1,2), (2,4), (3,6), y = 0 + 2x
- Similar to the covariance equation:
Future classes will expand on this to cover new conditions:
- Prevent over fitting on the input data
- Use some sort of error function
🔗 -> Links
Resources
- Put useful links here