Last updated 5/7/23 by Emily Hsi, created by Matthew Tang and Christianna Xu Asymptotics Runtime $\Theta$: there exists positive constants k1 and k2 such that $k_1f_1(N)\leq R(N)\leq k_2f_2(N)$ Important sums:Sum of first $n$ natural numbers = $\frac{n(n+1)}{2} \implies \Theta(n^2)$ Sum of first $n$ powers of 2 = $2Q -1 \implies \Theta(n)$ $\Sigma_{i=0}^{N-1} 2^i \implies \Theta(2^n)$ $1 + ... (\frac{n}{2})^2 + n^2 \implies \Theta(n^2)$ $1 + 2 + 4 + 8 + ... N^2 \implies \Theta(n^2)$

#1: Tutorial Part A: Convert 20-1-16-5 using letters to numbers to get TAPE (The title The Amazon Code clues A-Z) Part B: The flavortext insightful and title clues you to think of braille. It spells out EYES Part C: You are given a string eRkeU3_KF1w which is a YouTube URL (As clued by the title). The video is the puzzle recap from 2021, and in the description it says: If you're here from the 2022 puzzle game, the word is "mmbernie" giving MMBERNIE Part D: We are given C2, B3, A1, A2. The letter indicates which part to take the answer from and the number is the index of the answer. This gives us META Answer: META Answer explanation: Part D is a meta puzzle Title explanation: This is a tutorial puzzle

Originally played on 3/19/21 Overall I think the game turned out fine. Time allotments seemed good for the difficulty of the structures Wide variety in final builds BB8: Rubric seemed very fair Starship:

Final notes can be found here Lecture 1: 8/29/19 Designing Rational Agents Agent - entity that perceives and acts Rational agent- select actions to max utility Percepts, environment, action space dictate techniques for rational actions Lecture 2: 9/3/19

MT1 Notes can be found here MT2 Notes can be found here Final Notes can be found here Lecture 1: 8/28/19 Cycles Two pointers, advance p1 twice, advance p2 once. If they ever end up at the same place, report cycle. If no cycle, slow pointer never catches fast cycle If cycle, pointers will enter cycle at some time and dist will decrease between the pointers

Lecture 1: 1/20/21 Decision boundary has d-1 dimensions Training set error: Fraction of training images not classified correctly Test set error: Fraction of misclassified NEW images, not seen during training Outliers: points whose labels are atypical Overfitting: When test error deteroirates because classifier is sensitive to outliers Validation data is taken from training data Train classifier with different hyperparams

Lecture 24: 10/20/20 Memory and caches use powers of 2 for binary prefixes add bi so kibi is Ki for 1024 ceil log_2(x) DRAM speed is much lower than CPU Performance (performance gap) Use a memory cache (copy of a subset of main memory) for faster access

Links Frontend: Live scoreboard: here Defense submit gform: here Attack submit gform: here Backend:

Lecture 1: 8/26/20 Intro class, no notes Lecture 2: 8/28/20 Refer to table for easy hex to binary conversion Numerals have infinite preceeding zeroes Numbers are representations of numerals Numbers are theoretical

Lecture 18: 4/7/20 Recovery Atomicity, Durability Roll back changes that violate consistency If transactions ongoing on crash, abort them SQL has savepoints Release savepoint, rollback to savepoint

Lecture 10: 3/1/20 Iterators Setup, init, next, close Pull-based computation model: propagates down the graph Encapsulation - any iterator can be input to another State - iterators can maintain substantial internal states Like partitions for sorting/hashing

MT1 Notes: https://hackmd.io/@matthewtang/126mt1 MT2 Notes: https://hackmd.io/@matthewtang/126mt2 Lecture 9: 2/18/20 Weak Law of Large numbers $M_n = \frac{1}{n} \sum\limits_{i=1}^n X_i$ $X_i$ has mean $\mu$, var $\sigma^2$ $E[M_n] = \frac{1}{n}E[X_i]n = \mu$ $var(M_n) = \frac{1}{n^2} \sum\limits_{i=1}^n var(X_i) = \frac{\sigma^2}{n}$

Lecture 1: 1/22/20 SQL is declarative (say what you want, not how to get it) Turing complete, not functional Can call to other languages to extend SQL Database: set of named relations Relation/table: Schema: description/metadata Instance: set of data satisfying the schema

MT1 Notes: https://hackmd.io/@matthewtang/126mt1 MT2 Notes: https://hackmd.io/@matthewtang/126mt2 Lecture 2: 1/23/20 Birthday paradox (Complement) Probability that at least 2ppl in a group share the same bday Assume 365 days in a year (=k) $|\Omega| = k^n = 365^n$ A = "At least 2 people have the same bday"

# CS188 Notes Part 2 # Lecture 13: 10/15/19 ## Uncertainty * Observed variables: Agent knows things about the state (sensor readings) * Unobserved variables: Agent reasons about other aspects (where an object is) * Random variables * Note that $+r$ means True, $-r$ means False * Probability distributions * Marginal distributions: sub-tables that eliminiate variables * "Summing out" is marginalization over a variable * Conditional probabilities * $P(a|b) = \frac{P(a,b)}{P(b)}$ * Select

# CS170 Final Notes 1. MT1 Notes can be found [here](https://hackmd.io/@matthewtang/B1js2XPrS) 2. MT2 Notes can be found [here](https://hackmd.io/@matthewtang/B1NCuHWdB) 3. Final Notes can be found [here](https://hackmd.io/@matthewtang/r1PACERcH) # Lecture 26: 11/4/19 ## NP and P Problems * Classes of computational problems * P - Class of problems that you can find the solution in polynomial time * NP - Class of problems for which you can verify the solution in polynomial time * Example: 3CO

# CS170 MT2 Notes 1. MT1 Notes can be found [here](https://hackmd.io/@matthewtang/B1js2XPrS) 2. MT2 Notes can be found [here](https://hackmd.io/@matthewtang/B1NCuHWdB) 3. Final Notes can be found [here](https://hackmd.io/@matthewtang/r1PACERcH) # Lecture 13: 9/27/19 ## Horn SAT * SAT formula = satisfiability formula * Any formula with clauses that you must satisfy * A Horn SAT consists of implications and negative clauses * Implications * AND of positive literals imply 1 positive literal

# CS188 MT Study Guide Last updated 10/11/19, created by Matthew Tang # Uninformed Search * A type of planning problem * Search problem: * State Space: * World state contains all details of environment * Search state contains bare minimum for planning * Successor function: * Start State * Goal Test | Search | Description | Advantages | Disadvantages | | -------- | -------- | -------- | -------- | | Depth-First Search | Expand deepest nodes first | Better space |

# Interaction Lab Accomplishments ## Part 1: Autonomous Actions - Created a ROS package with autonomous actions - AR Tags: detection, ability to follow an AR tag (PD controller) - Audio: Locate audio and turn towards source (max norm algorithm) - Vision: Find and follow a colored line (OpenCV) - Use a HSV filter to find the appropriate color - Find the center of mass of the largest contour and follow that using a PD controller - Movement: - Autonomous movement - Potential field controller

# Backend Endpoints Server accepts only GET requests. URL: `https://c1hack.localtunnel.me` ### login [DONE] Returning users login to the app here ###### Endpoint ``` localhost:4000/raw ``` ###### Parameters - **<code>String</code> username** - **<code>String</code> password** ###### Return Format - **<code>JSON</code> userDetails** - See example below: ```json { status: true/false, username: username, name: name, events: { event1: event1_name, event2: event2_name }