TopicCheckIn_2.3.15.txt

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Probability and Inference
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Be able to (roughly) define and explain the significance of:

Sample spaces
Outcomes
Events
Trials
Random Variables
Sampling with(out) replacement
Binomial coefficient
Discrete Random Variables
Continuous Random Variables
Probability Mass (Distribution) Functions
Cumulative Distribution Functions
Support (of a Random Variable)
Conditional Probability and the Independence of Random Variables
Likelihood Functions
Maximum Likelihood Estimates
Likelihood Ratios
*Likelihood Principle
i.i.d.
Mean
Variance

Answer questions like these:

How do we define probabilities? What properties must they have?
How do we interpret probabilities? There are two major schools of thought on this.
How do events relate to outcomes?
How do random variables relate to events?
How are discrete and continuous random variables different?
How are likelihoods the same as/different from probabilities? 
How is the binomial related to the Poisson?
How is the geometric related to the exponential?
How are the Poisson and exponential related? [Hint: In the same way as the binomial and geometric.]

Be able to explain when these distributions are relevant and have a sense for their behavior:

Bernoulli
Binomial
Uniform (discrete and continuous)
*Poisson
Geometric
*Exponential

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Python
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Understand these terms:

statement
function
method
argument
return type
class
instance
typecasting
doc strings

Understand the nature of these simple data types:

int
float
string
Boolean

Create and manipulate (when possible) these complex data types:

list
tuple
dictionary
* sets 

Understand these flow control statements:

if...elif...else
for loops
while loops

Be able to define new functions with(out) arguments that may or may not have default values.

Be able to add single-line and multi-line comments to a program.