diff --git a/370/.gitignore b/370/.gitignore
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+*swp
diff --git a/370/notes/1.md b/370/notes/1.md
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+# lec1
+> trees & tree heaps 
+> also: ilearn still busted but theres a survey to fill out
+
+bin search trees
+max/min/heap trees
diff --git a/370/notes/2.md b/370/notes/2.md
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+# Divide and Conquer
+
+Make big problem into smaller problems to solve
+
+## Merge Sort
+
+_yote_
+
+1. find midpoint of list
+2. split into two smaller lists
+3. keep splitting smaller lists until we have a bunch of 1/2 size lists
+4. sort each using the _smaller_ already sorted values against each other into a new sub-list
+5. combine and sort each thing
diff --git a/370/notes/adj-list.md b/370/notes/adj-list.md
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+# Adjacency list
+
+Imagine 8 nodes with no connections
+
+To store this data in an _adjacency list_ we need __n__ items to store them.
+We'll have 0 __e__dges however so in total our space is (n+e) == (n)
+
+# Adjacency matrix
+
+space: O(n^2)
+The convention for notation btw is [x,y] meaning:
+	* _from x to y_
+
+# Breadth first search
+
+add neighbors of current to queue
+go through current's neighbors and add their neighbors to queue
+add neighbor's neighbors
+	keep going until there are no more neighbors to add
+go through queue and start popping members out of the queue
+
+# Depth first search 
+
+Here we're going deeper into the neighbors
+
+_once we have a starting point_ 
+
+_available just means that node has a non-visited neighbor_
+if available go to a neighbor
+if no neighbors available visit
+goto 1
+
+# Kahn Sort
+
+
+# Graph Coloring
+
+When figuring out how many colors we need for the graph, we should note the degree of the graph
diff --git a/370/notes/avl-trees.md b/370/notes/avl-trees.md
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+# Self balancing trees aka AVL trees
+
+_time complexity will be in terms of height_
+
+> Height:
+	distance from nulls after leaves
+	* impl: _root will typically have the largest height value_
+
+> Balance:
+	| left_height - right_height |
+	we can discard the || if we want negative balance vals but it really shouldn't matter
+	basically: we want the balance for each node to be 1 or 0.
+
+# Self balancing part
+
+We maintain balance through the tree's lifetime as we insert/delete things into the tree
+
+> Insertion/Deletion
+	* just like BST but we balance starting from the new node
+
+# Restoring balance
+
+There are 4 special cases when dealing with restoring balance:
+
+	1. left-left
+	2. left-right
+	3. right-left
+	4. right-right
+
+# Tries
+
+
diff --git a/370/notes/big-o.md b/370/notes/big-o.md
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+# Calculating Big O
+
+Suppose we have the following function
+
+```
+func(x):
+	if cond1 
+		O(n^2)
+	elif cond2
+		O(n)
+	else 
+		O(1)
+```
+
+How do we define the _average_ time complexity:
+* any input which isn't setup to be _special_
+	1. linear search which always finds the first elementO(1)
+	2. linear search that doen't find anything/last elementO(n)
+
+With linear searches: finding an average item in the middle(somwhere) we have to cover: 
+	* x% of the n size list
+
+Since we drop the constants in time complexity expressions we thus end up with O(n).
+
+## Heap Sort
+
+Time Complexity Exercise
+
+* Best: 
+* Average:
+* Worst:
+* Space:
+
+## Insertion Sort
+
+Time Complexity Exercise
+
+* Best: 
+	* Already sorted meaning we go through the whole list once 
+	* O(n) - because we'll do the check into our left sub-list but we don't go into it leaving us as constant time spent per item ie c\*n
+
+* Average:
+	* Pretty close to n^2 just for the sake of 
+	* O(n^2)
+
+* Worst:
+	* Going through the whole list sorting everything(backwards)
+	* O(n^2)
+
+		* must go through N items for each item 
+* Space:
+
+# Notation
+
+Time Complexity: O(n)
+
+Space Complexity: OMEGA(n)
+
+Both Complexity: THETA(n)
+	* such a complexity only exists if O(n) == OMEGA(n)
diff --git a/370/notes/complex.md b/370/notes/complex.md
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+# Complexities
+
+yote we talkin about space and time complexity
+
+* Space
+
+How much space this shit takin up 
+
+* Time 
+
+How fast something it's gonna take
+
+* Both time/space complexity notation
+
+_O(?)_: this notation is used for both space and time complexity
+
+# Scale of Complexity
+
+O(n) we refer as a linear because the higheset order exponent is /1/
+
+The higher the max order of the expression the higher order the overall complexity(duh).
+
+Non-polynomial expressions are just non-polynomial expressions(yote).
+
+In other words the part most people care about is the highest order exponential value in the function/expression, since everything else _will_ be faster than that.
+
+If you want to jam jargon in there go ahead but don't overthink this tbh famalamasham.
diff --git a/370/notes/graphs.md b/370/notes/graphs.md
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+++ b/370/notes/graphs.md
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+# Graph
+
+_First of a few pages on graphs_
diff --git a/370/notes/sorts.md b/370/notes/sorts.md
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+# Cheatsheet for sorts
+
+_using pythonic terminology for these cases_
+
+> Bubble
+	* take the largest value and buble it to the top/front of the list
+
+> Insertion
+
+> Selection
+	* take smallest value and drop it to the front
+
+> Merge
+	* split the hell out of things and sort from the inside out
+	* goes from side to side
+
+> Heap
+	* grab max item and append it to a heap 
+
+> Quick
diff --git a/370/notes/space.md b/370/notes/space.md
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+++ b/370/notes/space.md
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+# Space complexity
+
+With function calls there is explicit space used.
+
+Implicit space used is about how much work is done to take up space 
+
+# Binary recursive search
+
+> Explicit space: O(c)
+
+This is the amount of space taken per call.
+We know how much space has to be allocated every call so we can say it would likely be constant
+
+> Implicit: O(log(n))
+
+Because again we will likely take up log(n) times to reach our destination an this comes from our rekked stackspace.
diff --git a/370/notes/tries.md b/370/notes/tries.md
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+++ b/370/notes/tries.md
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+# Tries - Prefix Trees(syntax trees in disguise)
+
+We can build grammers from symbols where symbols follow a set of rules to build on each other.
+Kinda like linguistic sytax trees but, exactly the same.
+_Individual syntaxes are combined to build grammers combine to build phrases, etc. etc._
+
+Instead of symbols we use _prefixes_ as our terminology, to build _words_.
+Terminally sequenced symbols are denoted by a _leaf_ flag. 
+
+# Deletion
+
+We don't actually remove things for trivial cases.
+Instead we turn off the leaf flag in the end target node
+
+if we have /bathe/ and /bathes/ as valid phrases and wanted to remaove /bathe/ from our language
+	* All we have to do is set the /e/ to off as a valid leaf.
+
+If instead we wanted to remove /bathes/ instead we would go to /s/ and then set it to off like before.
+	* The problem now is that /s/ is hanging and it doesn't have any children so we can remove it entirely
+	* If a toggled off node has children it means that it necessarily is part of a valid prefix so it can not be removed
diff --git a/370/past-midterms/m1.md b/370/past-midterms/m1.md
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+++ b/370/past-midterms/m1.md
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+# Practice things
+_just going through the practice midterm problems_
+
+
+1. Identifying sorting algorithms based on progression(_conceptual_)
+
+_bubble sort_: the bigger the value = the bigger the bubble
+	* compare two values from the start always, bubble up the bigger value
+		keep compmaring two adjacent values till you get to the end of the list
+	* kinda like gladiator m&m's
+
+_insertion sort_: we have the left sublist which maintains a ordered state always
+	* we walk along the unsorted list and try to drop our current value into the currently sorted left-hand sublist.
+	* Key points: 
+		left hand ordered sublist
+
+_quick sort_: here we push things up to the top immediately where they are then sorted against the left hand sub-list
+
+_heap sort_: make a giant heap(list) and just take the largest/smallest value and prepend/append to the """heap"""
+
+__reponse__: insertion sort
+Left side of progression is always sorted while the right isn't 
+Left side also get progressively larger
+
+2. How you know if a graph is undirected given a matrix(_code_)
+
+_empirically_: check the upper right half of the matrix and check if each coord pair is reflected across the main axis
+
+	1.	Take a coords pair value(_say True_)
+	2.	Flip the (x,y) so we have /x,y/
+	3.	If the /x,y/ value is the same as (x,y) then those two nodes are bidrectional(i.e. undirected)
+
+The _trick_ here is making sure we don't bother check the divisor axis or below by accident.
+
+3. Binary tree lookup(_code_)
+
+Recursion is the smollest way of doing this so just go with that.
+
+4. Quick sort partition(_code_)
+
+	* Just splitting lists again
+	* Checking for partition results as well
+
+5. Graph traversal(_code_)
+
+DFS/BFS is fair game
+List/matrix meme allowed so don't even bother with other trash
+
+6. Emperical Graph Traversal(_conceptual_)
+
+Time complexity of either dfs/bfs will be /v\*e/ or something like that
+
+7. Topological sort(_code_)
+
+big meme matrix/list allowed
+
+
diff --git a/370/past-midterms/midterm1.pdf b/370/past-midterms/midterm1.pdf
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index 0000000..d7fc03f
Binary files /dev/null and b/370/past-midterms/midterm1.pdf differ
diff --git a/370/samples/adj-matrix.py b/370/samples/adj-matrix.py
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index 0000000..e69de29
diff --git a/370/samples/graph.py b/370/samples/graph.py
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--- /dev/null
+++ b/370/samples/graph.py
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+#!/usr/bin/python3
+
+import random
+class Node:
+    def __init__(self, data:int):
+        self.data = data
+        # empty list o rama
+        self.refs = []
+
+    def __str__(self):
+        return f'{self.data}:{self.refs}'
+
+def new_node(data):
+    """
+    Provides a new node for a ref list or a basic s
+    """
+    return Node(data)
+
+def add_node(base, data):
+    """
+    Creates a new node for the base node to hook onto
+    """
+    base.refs.append(new_node(data))
+
+def show_children(node):
+    """
+    LMAO @ your life
+    """
+    print(node.refs)
+
+def populate(node):
+    """
+    Populate our node with some data
+    """
+    for i in range(1,random.randint(2,10)):
+        new_tmp = new_node(random.randint(0,100))
+        node.refs.append(new_tmp)
+
+def child_count(node):
+    print(f'{len(node.refs)}')
+
+if __name__ == "__main__":
+    root = Node(0)
+    for i in 'ABCDE':
+        add_node(root, i)
+
+    print(f'Children count of root: {len(root.refs)}')
+    for i in root.refs:
+        populate(i)
+        child_count(i)
+
+    print('Root node:')
+    show_children(root)
diff --git a/370/samples/tree.py b/370/samples/tree.py
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+++ b/370/samples/tree.py
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+#!/usr/bin/python3
+import random   # just for some basic population methods
+
+class Node:
+    def __init__(self,data):
+        self.data   = data
+        self.children = []
+
+    def __str__(self):
+        return self.data
+
+    def __repr__(self):
+        return f'{self.__str()}:{self.children}'
+
+def new_node(data):
+    return Node(data)
+
+def add_node(node, lr, val):
+    node.children.append()
+    return None
+
+def lvr(root):
+    """
+    Inorder traversal
+    """
+    # left sequence
+    if root.left is not None:
+        lvr(root.left)
+
+    # visit sequence
+    print(root.data)
+
+    # right sequence
+    if root.right is not None:
+        lvr(root.right)
+
+    return 
diff --git a/370/samples/trie.py b/370/samples/trie.py
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+++ b/370/samples/trie.py
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+# Syntax tree in disguise
+class Node:
+    def __init__(self, symbol=None, leaf=False, children={}):        
+        # root node doesn't need any actual data
+        self.symbol     = None
+        self.leaf       = leaf
+        # {key=char : value=Node}
+        self.children   = children
+
+class Trie:
+    def __init__(self):
+        # root should contain every letter so that we can pick our thing
+        root = Node()
+
+    #wrapper func
+    def traverse(self, func, phrase, _node):
+        # wew lad
+        def func(_phrase, _node):
+
+        return func(phrase)
+
+    @traverse
+    def insert(self, phrase, node):
+        pass
+
+    @traverse
+    def remove(self, phrase, node):
+        pass
+
+    @traverse
+    def checkPhrase(self, phrase):
+        pass