Removing raw HTML

content/notes/abstract2def.md

@@ -71,9 +71,9 @@ An ideal $J$ of a commutative ring is said to be a **prime ideal** if for any tw
 $$
 ab \in J \implies a \in J \text{ or } b \in J
 $$
-<u>Theorem:</u> If $J$ is a prime ideal of a community ring with unity $A$, then the quotient ring $A / J$ is an integral domain.
+**Theorem:** If $J$ is a prime ideal of a community ring with unity $A$, then the quotient ring $A / J$ is an integral domain.
 
 An ideal $J$ of $A$ with $J \ne A$ is called a **maximal ideal** if there exists no proper ideal $K$ of $A$ such that $J \subseteq K$ with $J \ne K$.
 
-<u>Theorem:</u> If $A$ is a commutative ring with unity, then $J$ is a maximal ideal of $A$ iff $A/J$ is a field.
+**Theorem:** If $A$ is a commutative ring with unity, then $J$ is a maximal ideal of $A$ iff $A/J$ is a field.
 

content/notes/algorithms/dynamic.md

@@ -49,7 +49,7 @@ IterFibo(n):
 
 Here the linear complexity becomes super apparent!
 
-<u>Interesting snippet</u>
+*Interesting snippet*
 
 "We had a very interesting gentleman in Washington named Wilson. He was secretary of Defense, and he actually had a pathological fear and hatred of the word *research*. I’m not using the term lightly; I’m using it precisely. His face would suffuse, he would turn red, and he would get violent if people used the term *research* in his presence. You can imagine how he felt, then, about the term *mathematical*.... I felt I had to do something to shield Wilson and the Air Force from the fact that I was really doing mathematics inside the RAND Corporation. What title, what name, could I choose?"
 

content/notes/dimensionalityreduction/optimalitycriteria.md

@@ -6,7 +6,7 @@ Falling under wrapper methods, optimality criterion are often used to aid in mod
 
 ## Akaike Information Criterion (AIC)
 
-AIC is an estimator of <u>relative</u> quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality  of each model relative to each other.
+AIC is an estimator of *relative<* quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality  of each model relative to each other.
 
 This way, AIC provides a means for model selection. AIC offers an estimate of the relative information lost when a given model is used. 
 
@@ -79,7 +79,7 @@ $$
 
 ## Deviance Information Criterion
 
-The DIC is a hierarchical modeling generalization of the AIC and BIC. it is useful in Bayesian model selection problems where posterior distributions of the model was <u>obtained by a Markov Chain Monte Carlo simulation</u>.
+The DIC is a hierarchical modeling generalization of the AIC and BIC. it is useful in Bayesian model selection problems where posterior distributions of the model was *obtained by a Markov Chain Monte Carlo simulation*.
 
 This method is only valid if the posterior distribution is approximately multivariate normal.
 

content/notes/quadraticcongruences.md

@@ -6,7 +6,7 @@ math: true
 
 ## Number of Solutions
 
-<u>For congruences mod 2</u>
+*For congruences mod 2*
 
 **Proposition 16.1**. Let $f(x) = ax^2 + bx + c$ with $a$ odd, and let $\Delta = b^2 - 4ac$ be the discriminant of $f(x)$. Then,
 

content/notes/realanalysis.md

@@ -64,7 +64,7 @@ Let $s_1 > 0$ be arbitrary, and define $s_{n + 1} = \frac{1}{2}(s_n + \frac{a}{s
 
 **Corollary:** If $X = (x_n) \subseteq \mathbb{R}$ has a subsequence that diverges then $X$ diverges.
 
-**Monotone Sequence Theorem**: If $X = (x_n) \subseteq \mathbb{R}$, then it contains a <u>monotone subsequence</u>.
+**Monotone Sequence Theorem**: If $X = (x_n) \subseteq \mathbb{R}$, then it contains a *monotone subsequence*.
 
 **Bolzano-Weierstrass Theorem:** Every bounded sequence in $\mathbb{R}$ has a convergent subsequence.