2025-05-01 00:14:54

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UNB/Year 5/Semester 2/CS3383/Final Info.md

@@ -0,0 +1,2 @@
+No notes allowed
+Calculator allowed

UNB/Year 5/Semester 2/CS3383/Lecture Notes.md

@@ -26,4 +26,13 @@ Greedy algorithms always optimize for the current best chest, ignoring any possi
 
 This can work in cases where a problem has a greedy property.
 ### Making Change Problem 
-In the case where the denominations are always larger/equal to 2 of the smaller denominations then it makes sense when giving change, to simply give the largest coin available.
+In the case where the denominations are always larger/equal to 2 of the smaller denominations then it makes sense when giving change, to simply give the largest coin available.
+
+### Fractional Knapsack Problem 
+Given a set S of n items, with each item i having a positive benefit factor, b, and a positive weight, w.
+
+Goal: Choose items with maximum total benefit but with weight at most W.
+
+If we are allowed to take factional amounts, then this is the fractional knapsack problem. The objective is to maximize the benefit given the amount divided by the weight, with the constraint that the amount is less than the total amount W.
+
+### Interval Partitioning: Greedy 

UNB/Year 5/Semester 2/CS3383/Tutorial Notes.md

@@ -0,0 +1,13 @@
+# Schedule Problem
+A single server has n customers to serve. The sere time t_i required by each customer i is known in advance. Goal is to minimize T, the time in the system for customer i.
+
+For example, t_1=5, t_2=10, t_3=3. Schedule 1: 123, Schedule 2: 231
+
+Input: The set C of all customers C = { t_1, ... }. t_i is the service time for customer i
+Output: The total time in the system for all customers 
+Merge Sort C in ascending order. A greedy solution works in this case.
+
+We can simply run through the sorted list and serve them in order.
+
+But if we assume the greedy algorithm is not optimal in this case
+Try to reduce total time T

UNB/Year 5/Semester 2/CS4613/Lecture Notes.md

@@ -3,3 +3,15 @@ TODO: Go over slide 2 stuff and fill in
 # Lecture 2
 ## S-Expressions
 ### Simplified Calculator Parser
+
+# Lecture 3: Local binding
+Stacker helps show the environment, heap allocations, and stackframes
+
+# Lecture 4: Functions
+
+# Lecture 5: Macros
+
+# Lecture 6-7: Objects
+Desugared objects, defining a function that takes a case for method arguments
+
+# Lecture 8: Types

UNB/Year 5/Semester 2/CS4725/Final Review.md

@@ -0,0 +1,102 @@
+# Instructions:
+12 FRQ, written answers
+3 MCQ, multiple choice
+1 Matching question, algorithms (role of algorithms)
+15 total questions (?)
+
+1 A4 size double sided hand written notes allowed (Important!!!)
+2 hour exam
+Partial marks allowed for partially correct answers 
+Bring a calculator (Important!!!)
+# Part 1 Important Questions
+Horn form for logic?
+Why are these conditions not solvable without a truth table?
+
+# Part 2 Important Questions 
+## 1
+Arithmetic assertions can be written in first order logic with the predicate symbol <, the function symbols + and x, and the constant symbols 0 and 1. Additional predicates can also be defined with bi-conditionals
+a) Represent the property "x is and even number"
+Ax Even(x) <=> Ey x=y+y
+b) Represent the property "x is prime"
+Ax Prime(x) <=> Ey,z x=y * z => y = 1 V z = 1  
+c) Goldbach's conjecture is the conjecture (unproven as of yet) that "every even number is equal to the sum of two primes". Represent this conjecture as a logical sentence.
+Ax Even(x)=> Ey,z Prime(y) /\ Prime(z) /\ x=y+z
+
+# 2
+Find the values for the probabilities a and b in joint probability table below so that the binary variables X and Y are independent
+
+| X   | Y   | P(X, Y) |
+| --- | --- | ------- |
+| t   | t   | 3/5     |
+| t   | f   | 1/5     |
+| f   | t   | a       |
+| f   | f   | b       |
+Due to probability being max 1, we know that a + b must be 1/5
+P(Yt)/P(Yf) = a/b = 3
+b = 1/20
+a = 3/20
+
+# 3
+idk where R comes from, look into slides about bayes theorem
+Show the three forms of independence in Equation (12.11) are equivalent
+P(a|b) = P(a) or P(b|a) = P(b) or P(a /\ b) = P(a) * P(b) / R(?)
+
+First two are logically the same, just inverted
+
+From bayes theorem
+P(a | b) * P(b) = P(a) * P(b) / R(?)
+
+P(a /\ b) = P(a | b) * P(b)
+
+# 4
+Consider the following propability distrobutions:
+
+| A   | P(A) |
+| --- | ---- |
+| t   | 0.8  |
+| f   | 0.2  |
+
+| A   | B   | P(B\|A) |
+| --- | --- | ------- |
+| t   | t   | 0.9     |
+| t   | f   | 0.1     |
+| f   | t   | 0.6     |
+| f   | f   | 0.4     |
+
+| B   | C   | P(C\|B) |
+| --- | --- | ------- |
+| t   | t   | 0.8     |
+| t   | f   | 0.2     |
+| f   | t   | 0.8     |
+| f   | f   | 0.2     |
+Given these tables and no other assumptions, calculate the following probabilities.
+a. P(a, ~b)
+ = P(a) * P(~b|a)
+ = 0.8 * 0.1
+ = 0.08 
+b. P(b)
+ = P(bt|a) * P(a) + P(bt | ~a) * P(~a)
+ = 0.9 * 0.8 + 0.6 * 0.2 
+ = 0.84
+# 5
+Let A and B be Boolean Random variables. You are given the following probabilities 
+P(A=true) = 0.5
+P(B=true |A=true) = 1
+P(B=true) = 0.75
+
+What is P(B=true|A=false)?
+
+# 6
+Consider the XOR function of three binary input attributes, which produces the value 1 if and only if an odd number of the three input attributes has value 1.
+
+Draw a minimal sized decision tree for the three input XOR function.
+
+Three layer decision three, A > B > C. Output of tree would be
+0 1 1 0 1 0 0 1 if on the left of the decision is always 0 and 1 is right
+
+# 7
+Consider the problem of separating N data points into +ve and -ve examples using a linear separator. Clearly this can always be done for N=2 points on a line of dimension d=1, regardless of how many points are labeled or where they are located (unless the points are in the same place)
+
+a) Show that it can always be done for N=3 points on a plane of dimension d=2 unless they are co-linear.
+
+b) Show that it cannot (or can we?) always be done for N=4 points on a plane of dimension d=2

UNB/Year 5/Semester 2/CS4725/Lecture Notes.md

@@ -35,4 +35,6 @@ A rational agent is not omniscient or clairvoyant. It is not guaranteed they wil
 - Model based reflex agents with state 
 - Goal-based agents
 - Utility based agents 
-All of these can be turned into learning types 
+All of these can be turned into learning types 
+
+A learning agent has 4 main components, a performance algorithm, a critic, a learning element, and a problem generator, which forms a feedback loop which enables the agent to learn to adapt to it's environment.

UNB/Year 5/Semester 2/CS4745/Lecture Notes.md

@@ -38,4 +38,21 @@ Multicore systems, ones which have the ability to compute multiple tasks at the
 
 Manycore systems, ones which are often a large number of cores tasked with the same problem, in a shared memory environment are often throughput oriented, with the amount computation being higher amortized over a period of time, or focusing on longer running tasks/computation.
 ### Distributed Memory 
-The tasks are not within the same memory space, and do not share memory addresses, and there is no need for communication between nodes
+The tasks are not within the same memory space, and do not share memory addresses, and there is no need for communication between nodes 
+## SIMD
+TODO: Go and insert tables/data from slides
+## Threads and Processes
+Threads are fundamental units of execution, with their own program counter, and implemented in most operating system, are schedulable entities.
+
+Processes are instances of a running program. Each process has its own memory space and cannot share memory between processes.
+
+Starting threads are a bit simpler, as assigning a process needs to be registered with the OS as its own PID, as well as creation of virtual memory space by the OS.
+
+## Multiprocessor Architecture Design 
+
+# Software Considerations
+Programming languages use building blocks and their order of execution to make a program, based on functions with input and outputs.
+
+JIT compilers can by using runtime data optimize for certain sub tasks. Juilia is JIT compiled to LLVM IR and then translated to machine code by LLVM.
+## Task Graph Model
+The main motivation for using this is for data dependancies, as we cannot use data that is not loaded.

UNB/Year 5/Semester 2/HIST3925/Breakout Questions.md

@@ -0,0 +1,9 @@
+# Develop a theory of technological development.
+What would you borrow from Bacon, Merton, and Kuhn to create your theory?
+
+Paradigm shifts are norm in technology as opposed to the chaos that Kuhn described, due to the exponential nature of technological growth (in a broad sense). New tech thought/innovation leaders are similar to the most popular scientists in the age of science. In terms of competition technology is unbounded by the restraint of gifting knowledge, more focused on gaining wealth and hoarding knowledge for profit/power, with technological innovation concentrating on those with the most resources like big companies. Technologies are invented not to benefit the populous but to create wealth/power for the inventors.
+
+# Defend one of the four theories of why the great divergence happened as the critical theory 
+Capitalism and Exploitation
+
+Transitioned the ruling class from that of Divine Kings by birth and nobility, to that of wealth, often with overlap. Capitalism and exploitation brought about a search for cheap materials and labour. It promoted mobility of European populations by using external labour markets, often cheaper than European labour.

UNB/Year 5/Semester 2/HIST3925/Guest Lecture Notes.md

@@ -0,0 +1 @@
+# Aquaculture

UNB/Year 5/Semester 2/HIST3925/Lecture Notes.md

@@ -0,0 +1,31 @@
+## Philosophy of Science
+Francis Bacon believed that the previous study of science was flawed, as it believed that truth could be obtained through debate and philosophy. He envisioned a model of science based on experimentation and results, and study of the natural world.
+
+This model was later applied to many disciplines in the 19th century, such as political science, social science, computer science and changed things again.
+## Sociology of Science 
+Robert Burton showed us how the essence of scientific life is competitive to gain priority in discovery. This creates ambivalent attitudes in scientists and academia, as the system doctrine says that importance or glory is not the point of the ordeal, but the system itself re-enforces the reward system.
+
+Databases changed how the field was studied, as now with everything being cataloged digitally, you could now very easily perform statistical analysis and examine how science was performed.
+
+The study revealed that academia or science was a pyramid, with only a small number of scientists producing the most amount of research and only a small number of that research was cited in other research, i.e, important.
+## History of Science
+Thomas Kuhn came out with a book called "The Structure of Scientific Revolutions", which claimed that science must not focus on the individual scientists but on disciplinary communities that decide on the context of their research.
+
+Scientists are to find the known not to discover the unknown as Kuhn says?
+
+### Kuhn's Conclusions
+Scientific Method's appeal to nature does not suffice to guide scientists to their most important decisions, i.e. you must have new ideas and cannot simply continue extenuated research
+
+We cannot say science makes progress or moves closer to truth in passing through a revolution from one paradigm, to its successor.
+
+Come back to this and review
+## Gender and Science 
+
+# Industrial Revolution 
+Transition
+- Factory System (important!): A transition from artisans who produced the whole product to an individual worker who did a fraction of the overall work to manufacture a product 
+- English working class (farmers to factory workers)
+
+# For Test 2
+Social Shaping of Technology Theory
+Breakout group questions from 2025-03-26

UNB/Year 5/Semester 2/HIST3925/Presentation Notes.md

@@ -0,0 +1,8 @@
+# Group 1
+Women/Non-binary in STEM
+Interesting things
+- Statistics?
+- Personal Experience
+- Kahoot
+- Discussion Questions
+