commit b9b70067784cd72b9c44251b11bb5f1c76242e4b
parent 6a64183b162c0dc130a3ecd9f85d104060f821af
Author: Andrew <andrewlaack1@gmail.com>
Date: Mon, 3 Jun 2024 19:25:43 -0500
took notes today
Diffstat:
16 files changed, 165 insertions(+), 0 deletions(-)
diff --git a/BinomialDistribution.md b/BinomialDistribution.md
@@ -0,0 +1,12 @@
+:stats:
+# Binomial Distribution
+
+Stats D1
+
+## Notes
+
+**Definition:** A binomial distribution is a distribution such that each point is the probability of some true or false condition.
+
+This can be thought of as a medical experiment. The x-axis would be some marker and the y axis would be the probability of curing some disease... As an example.
+
+Each repition is called a trial (single example).
diff --git a/Ensembles.md b/Ensembles.md
@@ -0,0 +1,10 @@
+:ml:
+# Ensembles
+
+CH2
+
+## Notes
+
+**Definition:** Ensembles are models composed of multiple other models.
+
+An example is a random forest regressor.
diff --git a/ExponentialDistribution.md b/ExponentialDistribution.md
@@ -0,0 +1,10 @@
+:stats:
+# Exponential Distribution
+
+Stats D1
+
+## Notes
+
+**Definition:** An exponential distribution is one that is decreasing at a decreasing pace. Specifically, it can be stated in some form of lambda^-x where there may be constants or other things involved, but we find that as x increases, y decreases at a decreasing rate.
+
+This is often used to show the probability of time between random things happening which is similar in some ways to [[PoissonDistribution.md]].
diff --git a/HHP102.md b/HHP102.md
@@ -0,0 +1,9 @@
+:index: :hhp102:
+# Health And Wellness
+
+Summer 24
+
+## Main Links
+
+[[TransTheoreticalModel.md]]
+[[SMART.md]]
diff --git a/KMeans.md b/KMeans.md
@@ -0,0 +1,15 @@
+:ml:
+# K-means Clustering
+
+ML CH2
+
+## Notes
+
+**Definition:** K-means clustering is a clustering algorithm that clusters data together by finding the mean distance from clusteroids and places said element into said cluster.
+
+Basic idea:
+
+1. Select cluster centroids
+2. Go through elements finding nearest centroid mean
+3. Add item to centroid and update the mean position
+4. Repeat Step 2
diff --git a/MachineLearning.md b/MachineLearning.md
@@ -80,6 +80,9 @@ Concepts:
[[MinMaxScaling.md]]
[[OrdinaryLeastSquares.md]]
[[RadialBasisFunction.md]]
+[[KMeans.md]]
+[[StochasticAlgorithm.md]]
+[[Ensembles.md]]
To do:
diff --git a/MonotonicFunction.md b/MonotonicFunction.md
@@ -0,0 +1,8 @@
+:stats:
+# Monotonic Functions
+
+Stats
+
+## Notes
+
+**Definition:** A monotonically increasing function is one where as the input increases the output either stays the same or increases. The inverse is also true with a monotonically decreasing function. The statement of monotonicity simply means always increasing or decreasing.
diff --git a/NormalDistribution.md b/NormalDistribution.md
@@ -0,0 +1,10 @@
+:stats:
+# Normal Distribution
+
+Stats D1
+
+## Notes
+
+**Definition:** A normal distribution is a unimodal one in which most observations cluster around the mound while fewer and fewer observations are farther away.
+
+
diff --git a/PoissonDistribution.md b/PoissonDistribution.md
@@ -0,0 +1,18 @@
+:stats:
+# Poisson Distribution
+
+Stats D1
+
+## Notes
+
+**Definition:** A poisson distribution is a common distribution that gives the probability of something happening at a point in time (or position or volume) where the probability of it happening at any given time is known.
+
+An example of this is the distribution of number of texts receieved in a day where the mean is 12 texts per day. Using this information we can then use a known formula, based on normal distributions, to find the probability that we receive 8 text in a day, less than 8 texts, or any other number of texts.
+
+Given this, a poisson distribution does not have an upper bound, but these events become exceedingly rare. As such, the graph has a right skew.
+
+The formula is as follows:
+
+p(x) = (lambda^x * e^-lambda)/(x!)
+
+In the above we have lambda as the rate of success and x is the number of successes.
diff --git a/Probability.md b/Probability.md
@@ -16,3 +16,5 @@ When we have a domain that is finite we then state we have a [[DiscreteProbabili
In practical terms, for u(X) X is the set off outcomes that are possible and the function returns the probability of said outcome.
**Sometimes we use u({1}) but often we use P(1) to describe the probability of 1. Notice that u({1}) takes in a set whereas P(1) does not require such statements.**
+
+Some syntax for ya, when stating P(H|Theta = 1/3) this means the probability of H given that theta = 1/3. This is often used when we don't know the probability of theta, but need to describe the situation. In this instance, theta is considered a parameter.
diff --git a/ProbabilityDensityFunctions.md b/ProbabilityDensityFunctions.md
@@ -8,3 +8,7 @@ Stats ch1
**Definition:** A probability density function shows the probability of outcomes for [[ContinuousProbability.md]] problems.
Think of KDEs and kind of histograms. The difference with histograms is they use bins instead of a continuous probability graph.
+
+Something to note, the area under the curve is the probability. As such, the likelihood of all values that come before some value is the integral over said range (Antiderivative. See fundamental theroem of calculus).
+
+Another property of a PDF is that the integral of -infinity to infinity is always equal to 1 and p(y) >= 0 for all y. This means there is never a negative probability and there is a 100% probability across the domain of the function.
diff --git a/SMART.md b/SMART.md
@@ -0,0 +1,21 @@
+:hhp102:
+# SMART Goals
+
+W2 H&W
+
+## Notes
+
+**Definition:** This is a type of goal setting that meets the following criteria:
+
+1. Specific
+ - Make the goal specific enough to measure success and set a timeframe
+2. Measurable
+ - Ensure the goal has a metric that determines success (yes/no or regression/classification)
+3. Actionable
+ - How will the goal be achieved (do we have ability to achieve the goal?)
+4. Relevant
+ - Make sure the goal is relevant to our life
+5. Time Bound
+ - Include date when something should be done by.
+
+I should actually do this shit.
diff --git a/Statistics.md b/Statistics.md
@@ -11,4 +11,10 @@ Links to Stats Notes
[[Probability.md]]
[[SetFunction.md]]
+[[MonotonicFunction.md]]
+[[ProbabilityDensityFunctions.md]]
+[[BinomialDistribution.md]]
+[[PoissonDistribution.md]]
+[[ExponentialDistribution.md]]
+[[NormalDistribution.md]]
diff --git a/StochasticAlgorithm.md b/StochasticAlgorithm.md
@@ -0,0 +1,10 @@
+:ml:
+# Stochastic Algorithm (Stochastic Optimization)
+
+ML CH2
+
+## Notes
+
+**Definition:** A stochastic algorithm is an optimization algorithm that uses randomness.
+
+One example of this is [[KMeans.md]] which picks random cluster centroids.
diff --git a/TransTheoreticalModel.md b/TransTheoreticalModel.md
@@ -0,0 +1,26 @@
+:hhp102:
+# Trans-Theoretical Model of Behaviour Change
+
+W2
+
+## Notes
+
+**Definition:** This is a model that describes the process of enacting behavior changes.
+
+Stages:
+
+1. PreContemplation
+ - Not ready to make changes
+ - Might be down or defensive
+2. Contemplation
+ - Getting ready
+ - Intent to engage in next few months (up to 2 years)
+ - Know positives but might avoid action
+3. Preparation
+ - Ready to engage in action within 30 days
+ - Begin to take steps to integrate steps
+4. Action
+ - Doing the behavior
+ - Need to keep working hard to keep consistency
+5. Maintenance
+ - Behavior has been changed
diff --git a/index.md b/index.md
@@ -8,6 +8,7 @@ This is the index for my main note classifications. I will maintain this as a ho
[[CS202.md]]
[[CS331.md]]
[[BIOL115.md]]
+[[HHP102.md]]
[[Math310.md]]
[[TexRef.md]]
