Cs 246 stanford. Operated by cs-oh-queue-dev and CSD-CF at Stanford.

Cs 246 stanford %PDF-1. lsh. dev. Q: How do I submit my assignment? Based on different applications: Recommender systems Market basket analysis Link analysis, spam detection Duplicate detection and similarity search Web advertising Based on different “tools”: Linear algebra: SVD, Matrix factorization Optimization: Stochastic gradient descent Dynamic programming: Frequent itemsets Stanford students can see them here. Public resources: The lecture slides and assignments will 1/16/2014 Jure Leskovec, Stanford C246: Mining Massive Datasets 2 . The norm squared of each column is given by the diagonal entries of Q. Under what circumstances will w(r0 CS 246: Problem Set 4 Tony Hyun Kim March 7, 2012 1 Strategies for high-frequency trading 1. SCPD students can take exam either at Stanford or take it through the exam proctor and return scanned exam to cs246. cs-oh-queue-dev and CSD-CF at Stanford. Gradiance (no late periods allowed): GHW 1: Due on 1/14 at 11:59pm. Note that while the cameras are positioned with the intention of recording only the instructor, occasionally a part of your image or voice might be incidentally captured. Regular Office Hours: We will have several in-person office hours per week, starting from the 2nd week of the course. Friday 1/7 5-7pm: review of basic concepts of linear algebra, probability and statistics Tuesday 1/11 5-7pm: Hadoop Q&A session We will post the location and on the website and the mailing list soon 1/5/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 3 CS 240 is a graduate level course in operating systems, covering classic and new papers in virtual memory management, synchronization and communication, file systems, protection and security, extensions and fault tolerance, and the history and experience of systems programming. data Locality sensitive hashing Clustering Dimensional ity reduction Graph data PageRank, SimRank Network Analysis Spam Detection Infinite data Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program/algorithm (e. ; GHW 4: Due on 2/04 at 11:59pm. Prove that w(r0) = w(r). Students will learn how to implement data mining algorithms using Hadoop and Apache Spark, how to We will strictly enforce the Stanford Honor Code. Computer Science Department Language: English Physical Description: 1 text file Publication Info: Stanford (Calif. 1. , CS107 or CS145 or equivalent are recommended). Jure Leskovec & Mina Ghashami, Stanford University 2 Images Text/Speech Modern deep learning toolbox is designed for simple sequences & grids Familiarity with writing rigorous proofs (at a minimum, at the level of CS 103). HW2 solution. Companies place true value on individuals who understand and manipulate large data sets to provide CS 246: Mining Massive Data Sets The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data. , the things one Given a cloud of data points we want to understand its structure . Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality Handouts Assignments. CS 246: Problem Set 1 Tony Hyun Kim January 26, 2012 1 MapReduce 1. Familiarity with algorithmic analysis (e. Lectures: are on Tuesday/Thursday 3:00-4:20pm in person in the NVIDIA Auditorium. ZIPCODE A. And in most cases, looks are not deceiving. University Stanford University. Familiarity with the basic probability theory. The size of Ris m n, where mis the number of movies, and nthe number of users. All students will be given two no-questions-asked late periods, but only one late period can be used per-homework. Materials Notes and reading assignments will be posted on the course Web site. SUNet Login. Lecture Videos: are available on Canvas for all the enrolled Stanford students. Academic Calendar 2022-23 Schedule of Classes Bulletin Archive Get Help Academic Calendar 2022-23 Course Description. 1/8: There will be 3 optional recitation sessions for CS 246 this year. You are responsible for all material that appears here and should check this page for updates frequently. All students (non-SCPD and SCPD) should submit their assignments electronically via Gradescope. h(C) is small enough that we can fit a signature in main memory for each column 2. They will cover basic materials on MapReduce/Hadoop, Probability and Linear Algebra. Due on 01/13 at 5:00 pm. Locality-sensitive hashing: Focus on Stanford CS246H Winter 2018 CS246: Mining Massive Datasets Crash Course in Spark Daniel Templeton. The element R iu of this matrix corresponds to the rating given by user uto item i. Data mining overlaps with: Databases: Large-scale data, simple queries Machine learning: Small data, Complex models CS Theory: (Randomized) Algorithms Different cultures: To a DB person, data mining is an extreme form of analytic processing – queries that examine large amounts of Linear algebra review (courtesy CS 229) Linear algebra review slides CS 246: Mining Massive Data Sets. Stanford Office of Community Standards has more information. baskets Each basket is a . Regular Office Hours: We will have office hours every day, starting from the 2nd week of the course. , any of Math 51, Math 103, Math 113, CS 205, or EE 263). Math CS 246: Mining Massive Data Sets - Final 2 1 MapReduce [20 points] DISTINCT operator using Map-Reduce. edu (mcs-inquiries[at]lists[dot]stanford[dot]edu) Campus Map. 86% (42) 8. CS246 will discuss methods and algorithms for mining massive data sets, while CS341 (Advanced Topics in Data Mining) will be a project-focused advanced class with an unlimited access to a large MapReduce cluster. Readings for the class will be from: Networks of tightly connected groups Network communities: Sets of nodes with lots of connections inside and few to outside (the rest of the network) 2 Communities, clusters, groups, modules Handouts Sample Final Exams. The bold blue trace shows the result of removing bad trades, i. They will review basic material on Probability and Linear Algebra that you should have seen in prior coursework. 1 Special techniques In addition to the \pick an arbitrary element" trick, here are several other techniques com-monly seen in proofs. Go to course. This time you will work on a slightly diferent task. A late period ends at midnight, on the following Monday (this means that if the assignment is due on Thursday then the late period expires on the following Monday midnight, 11:59pm Pacific Time. A late-period CS 246: Mining Massive Data Sets - Final 2 1 MapReduce [20 points] DISTINCT operator using Map-Reduce. Stanford University Stanford Home; CS 246: Mining Massive Data Sets. Course information handout; Hadoop tutorial will help you set up Hadoop and get you started. Au 2022. (b) [7pts] Suppose there are still no dead ends, but we use a teleportation probability of 1 , where 0 < <1. Office hours will be held over Zoom. 3/9: SCPD students can take exam either at Stanford or take it through the exam proctor and return scanned exam to cs246. Please only use this Key idea: “hash” each column C to a small signature h(C), such that: 1. , CS 161 would be much more than necessary). Mining Massive Datasets Announcements: Important course information will be posted on this web page and announced in class. This course discusses data mining and machine learning algorithms for analyzing very large amounts of data. Assignments 96% (56) Save. The convergence criteria for the above algorithm is %cost< , where %cost= jf k 1(w;b) f k(w;b)j 100 Linear algebra review (courtesy CS 229) Linear algebra review slides (TBA) Linear algebra, probability, and proof techniques (from CS224W) Spark tutorial slides CS 246: Mining Massive Data Sets | Problem Set 1 2 The input le contains the adjacency list and has multiple lines in the following format: <User><TAB><Friends> Here, <User>is a unique integer ID corresponding to a unique user and <Friends>is a comma-separated list of unique IDs corresponding to the friends of the user with the unique ID <User>. Minhashing: Convert large sets into short signatures, while preserving similarity 3. 96% (56) 15. I have neither given nor received unpermitted help on this examination. edu If you are auditing send us email and we will subscribe you! Office hours: Jure: Tuesdays 9-10am, Gates 418 See course website for TA office hours. Homework 1: Out on 1/8. 1/19/2015 Jure Leskovec, Stanford C246: Mining Massive Datasets 3 12 Clustering in two dimensions looks easy Clustering small amounts of data looks easy And in most cases, looks are not deceiving Many applications involve not 2, but 10 or 10,000 dimensions High-dimensional spaces look different: Almost all pairs of points are very far from each other --> The Curse of Dimensionality! 1/18/22 Jure Leskovec & Mina Ghashami, Stanford CS246: 1/8: There will be 3 optional recitation sessions for CS 246 this year. This combines best of machine learning, statistics, artificial intelligence, databases but more stress on Scalability (big data) Algorithms Computing architectures Automation for handling large data CS 246: Mining Massive Data Sets Problem Set 1 2 Tips: Use Google Colab to use Spark seamlessly, e. Given two sets. stanford. 17 Documents. Mining Massive Datasets (CS 246) Prepare your exam. The emphasis will be on Map Reduce as In Gradiance quizzes and homework you have seen matrix-vector multiplication in MapReduce. Reduction (SVD, CUR)) Clustering Recommender systems PageRank and TrustRank Machine Learning: kNN, SVM, Decision Trees Mining data streams Advertising on the Web 3/9/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 3 cs246-win1011-staff@lists. 1 Similarity by \hand" I did the rst iteration by hand (really), and realized that I was not su ciently awake to carry out the computation for three more iterations without making a mistake. Follow this course. Topics include: Frequent itemsets and Association rules, Near Neighbor See more There will be 10 Colabs in total: Colab 0 (Spark tutorial), and Colab 1 to 9 (released weekly). of . Clustering small amounts of data looks easy. g. If you are having trouble registering for these services under your SUNetID, please send an email to the course staff mailing list during the first week of class, so that we may We can feed these embeddings into any loss function and run SGD to train the weight parameters ℎ : the hidden representation of node R at layer H: weight matrix for neighborhood aggregation : weight matrix for transforming hidden vector of Supplement to CS 246 providing additional material on the Apache Hadoop family of technologies. of last k elements (4) Counting frequent items 2/25/2015 Jure Leskovec, Stanford CS246: Mining High dim. edu ABSTRA CT Prediction tasks ov er nodes and edges i n netw orks require careful Course information: This course is the first part in a two part sequence CS246/CS341 replacing CS345A: Data Mining. 80 80 students. ; GHW 7: Due on 2/25 at 11:59pm. Companion course CS246H: There is a companion course CS246H, which is completely independent from CS246 and covers Spark programming. Course website: http://cs246. items e. , B[h(s)]=1 Hash each element a of the stream and 1/5: There will be 2 optional recitation sessions for CS 246 this year. Teaching Assistant, Spring 2023, School of Engineering, Stanford University, 2023 This course offers an in-depth exploration of data mining and machine learning techniques for analyzing extensive datasets, focusing on big data systems, link analysis, similarity search, and methods for large-scale data processing, including recommender CS 246 { Review of Linear Algebra 01/17/19 1 Linear algebra In this section we will discuss vectors and matrices. HW4 solution. Machine Learning with Graphs CS 224W (Win) Mining Massive Data Sets CS 246 (Spr) 2021-22 Courses. So, I wrote a short Matlab script (attached) to compute the similarity. , the things one customer buys on one day A general many-many mapping (association) between two kinds of things Given a set of keys S that we want filter Create a bit array B of n bits, initially all 0s Choose a hash function h with range [0,n) Hash each member of s∈ S to one of m buckets, and set that bit to 1, i. , GNNs Rule of thumb: If working on a well understood problem use deep learning. com Office Hours: Tuesday 1:00-2:30pm, Friday 10:30am-12:00pm. CS246 Win2020 HW1-2 - hw1solution. 2011 final exam with solutions; 2013 final exam with solutions; Assignments. , CS 161). Q: How do I scan and create a PDF from a set of handwritten notes? Many printers and photocopiers have a create PDF feature. edu 24 Permute the rows of the Boolean matrix using some permutation The availability of massive datasets is revolutionizing science and industry. edu Course mailing list: cs246-win1011-all@lists. Sp 2022. stanf ord. 1 Similarity Matrix We can consider the cosine similarity to be a dot product between two normalized vectors u=jjujjand v=jjvjj. CS 246: Mining Massive Data Sets. ID A. Instructor: Jure Leskovec CS 246: Problem Set 3 Tony Hyun Kim February 23, 2012 1 Similarity Ranking 1. ; Computer science Subject: Stanford University. py # Authors: Jessica Su, Wanzi Zhou, Pratyaksh Sharma, Dylan Liu, Ansh Shukla import numpy as np import random import time import pdb import unittest from PIL import Image # Finds the L1 distance between two vectors # u and v are 1 CS 246 (Stanford) Course Assistant, Graduate Mining Massive Datasets. Mining Massive Data Sets CS 246 (Win) 2022-23 Courses. small subset of items e. Jure Leskovec, Stanford C246: Mining Massive Datasets node2vec: Scalab le Feature Learning for Netw orks Adity a Gro ver Stanf ord Univ ersity adity ag@cs . Late assignments: Each student will have a total of two late periods to use for homeworks. ; GHW 6: Due on 2/18 at 11:59pm. Ask AI. CS 246 { Review of Linear Algebra 01/17/19 1 Linear algebra In this section we will discuss vectors and matrices. ) Imprint: Stanford (Calif. Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality CS 246: Mining Massive Data Sets - Problem Set 3 2 (a) [6pts] Suppose the Web has no dead ends. However, Mining Massive Datasets (CS 246) 17 Documents. Note that the items are represented as the columns of R. Sim(C 1, C 2) is the same as the “similarity” of h(C 1 Logistics. Any inanimate materials may be used, including laptops or other computing devices. Highest rated. 1 Vectors and vector operations A vector is a one dimensional matrix, and it can be written as a column vector: v = 2 6 6 6 4 v 1 v Stanford Office of Community Standards has more information. (CS109 or Stat116 or equivalent is sufficient but not necessary). Academic year: 2019/2020. Documents; Q&As; Other (4) Test Prep (1) Showing 1 to 5 of 5. Each one of them is worth 3%. It meets Wednesdays 11:30AM - 1:20PM, in CS 246: Mining Massive Data Sets - Problem Set 3 2 Suppose we are given a matrix Rof recommendations. Colab 0 is solved in real time in the first Recitation Session video. High dim. Prepare your exam. CS 246: Mining Massive Data Sets - Problem Set 2 2 Principal Component Analysis (PCA): Given a data matrix M (of size p q), PCA involves the computation of the eigenvectors of MM Tor M M. The key idea is that if two people have a lot of mutual friends, then the system should recommend that they connect with each 4 Clustering in two dimensions looks easy. 2 Stanford CS246H Winter 2018 The Plan Based on different applications: Recommender systems Market basket analysis Link analysis, spam detection Duplicate detection and similarity search Web advertising Based on different “tools”: Linear algebra: SVD, Matrix factorization Optimization: Stochastic gradient descent Dynamic programming: Frequent itemsets Hashing: LSH, Bloom filters. Note that the PDF size should be smaller than 30MB. 3 A large set of items e. Course information: This course is the first part in a two part sequence CS246/CS341 replacing CS345A: Data Mining. You can also use smartphone apps such as the Genius Scan app for iOS and Android to create a PDF. Students shared 17 documents in this course. Assignments 86% (42 A few reasons why this is important: They outperform DL models in certain taks. 1 Baseline: Mean reversion Fig. Familiarity with basic linear algebra (e. ; GHW 5: Due on 2/11 at 11:59pm. Instructor: Jure Leskovec Office: 418 Gates Office Hours: Tuesday 9:00-10:00am. It meets Wednesdays 11:30AM - 1:20PM, in Skilling Auditorium, and the lecture videos can be CS 246: Mining Massive Data Sets. These office hours are open to any enrolled students (including SCPD students) who are able to attend them in-person. HW3 2020 CS246 Solutions. (signed)_____ Di r e c ti o n s : The exam is open book, open notes. 1/13/2015 Jure Leskovec, Stanford C246: Mining Massive Datasets 4 [Hays and Efros, SIGGRAPH 2007] Based on different applications: Recommender systems Market basket analysis Link analysis, spam detection Duplicate detection and similarity search Web advertising Based on different “tools”: Linear algebra (SVD, Matrix factorization) Optimization (Stochastic gradient descent) Dynamic programming (Frequent itemsets) Hashing (LSH, Bloom filters) Supplement to CS 246 providing additional material on the Apache Hadoop family of technologies. Companion course CS246H: There is a companion course CS246H, which is completely independent from CS246 and covers Hadoop programming. AGE 1 12345 30 2 12345 40 3 78910 10 4 78910 10 5 78910 20 Computer Science (CS) Approved courses: Any CS course numbered 110 or above; An advanced CS course, such as CS 246 (3 units) An advanced MS&E course, such as MS&E 220 or 223 (3 units) lists. Most of the elements of the matrix are unknown because each user can only rate a For each node, enumerate over all features For each feature, sort the instances by feature value Use a linear scan to decide the best split along that Browse to the SCPD platform if you are a Stanford student, or check our public mirror. CS 448B (Stanford) Course Assistant, Graduate Data Visualization. These office hours are open to any enrolled students (including SCPD students). 96% (56) 19. Q: Will the recitation sessions be recorded? A: Yes, the recitation sessions will also be available along with the other recorded lectures. Move input data GPU1 Run forward pass Move activations from GPU1 to GPU2 Run forward pass Move activations from GPU2 to GPU3 Run forward pass Compute loss Run MapReduce Association Rules Finding Similar Items Locality Sensitive Hashing Dim. 1about a bandit with arms 1-5, reporting the current estimated payoff with CS 246: Mining Massive Data Sets - Problem Set 2 5 Based on the experiment and the expressions obtained in part (c) and part (d) for M TM, what is the relationship (if any) between the eigenvalues of M M and the singular values of M? Familiarity with writing rigorous proofs (at a minimum, at the level of CS 103). Many applications involve not 2, but 10 or 10,000 dimensions. mmds@gmail. edu Lecture slides (at least 6h before the lecture) Announcements, homeworks, solutions Readings! CS246 Final Exam, Winter 2019 5 4 Learning Through Experimentation [10 points] Figure 1: Estimated payoff with confidence interval plot. ), December 1, 1978 Genre: technical reports Identifier: CS-TR-1978-683 Collection: Since we can not store the entire stream, one obvious approach is to store a sample Two different problems: Sample a fixed proportion of elements in the stream (say 1 in 10) Maintain a random sample of fixed size over a potentially infinite stream CS 246: Mining Massive Data Sets. ) A: Yes, if you are a Stanford student. Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality Assume we have a graph 𝑮: is the vertex set 𝑨 is the adjacency matrix (assume binary) 𝑿∈ℝ ×|𝑉| is a matrix of node features R: a node in ; R: the set of neighbors of R. MapReduce (Hadoop) Programming model designed for: Large Datasets (HDFS) Large files broken into chunks Chunks are replicated on different nodes Easy Parallelization Takes care of scheduling Fault Tolerance Monitors and re-executes failed tasks. , things sold in a supermarket A . Familiarity with writing rigorous proofs (at a minimum, at the level of CS 103). , any of Math 51, Math 103, Math 113, CS 205, or EE 263 would be much more than necessary). The expression for the next estimate of r ibecomes r0 i = P n j=1 M ijr j+(1 )=n. AGE 1 12345 30 2 12345 40 3 78910 10 4 78910 10 5 78910 20 Mining Massive Datasets (CS 246) 17 Documents. The DISTINCT(X) operator is used to return only distinct (unique) values for datatype (or column) X in the entire dataset . TAs: We have 9 great TAs! Sean Choi (Head TA), Sumit Arrawatia, Justin Chen, Dingyi Li, Anshul Mittal, Rose Marie Philip, Robi Robaszkiewicz, Le Yu, Tongda Zhang Office hours: Jure: Wednesdays 9-10am, Gates 418 See course website for TA office hours For SCPD students we will use Google Hangout We will post Google Hangout links on Piazza Data mining overlaps with: Databases (DB): Large-scale data, simple queries Machine Learning (ML): Small data, Complex models CS Theory: (Randomized) Algorithms Different cultures: To a DB person, data mining is an extreme form of analytic processing – queries that examine large amounts of data Stanford; Mining Massive Datasets; Mining Massive Datasets (CS 246) 17 17 documents. High-dimensional spaces look different: almost all pairs of points are at about the same distance 1/29/2012 Jure Leskovec, Stanford C246: Mining Massive Datasets • Familiarity with writing rigorous proofs (at a minimum at the level of CS 103). The availability of massive datasets is revolutionizing science and industry. Instructor: Jure Leskovec Familiarity with writing rigorous proofs (at a minimum, at the level of CS 103). 5 %¿÷¢þ 15 0 obj /Linearized 1 /L 146057 /H [ 1034 187 ] /O 19 /E 115314 /N 6 /T 145699 >> endobj 16 0 obj /Type /XRef /Length 89 /Filter /FlateDecode 5/18/23 Jure Leskovec & Mina Ghashami, Stanford University 13 data science spark Stanford learning document 110 15 3 0 10 document 20 9 2 8 2 document 31 2 20 0 4 document 414 11 1 32 2 document 55 1 7 12 5 document 66 3 5 1 1 document 72 3 5 2 7 CS 246: Mining Massive Data Sets - Problem Set 4 2 is the learning rate of the gradient descent, and r w(j) f(w;b) is the value computed from computing equation (2) above and r bf(w;b) is the value computed from your answer in question (a) below. First do integer encoding, then create a binary vector that represents the numerical values Ex: following integer encoding on provider: Netflix -> 1, Prime Video -> 2, HBO Max ->3 , Hulu -> 4 These recordings might be reused in other Stanford courses, viewed by other Stanford students, faculty, or staff, or used for other education and research purposes. • Familiarity with algorithmic analysis (e. , CS107, CS161 or CS145 or equivalent are recommended). CS 149 (Stanford) Course Assistant, Parallel Computing. All students (SCPD and non-SCPD) must submit We will strictly enforce the Stanford Honor Code. 87% (23) 9. Supplement to CS 246 providing additional material on the Apache Hadoop family of technologies. Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality Your Piazza email address should be daffyduck@stanford. edu. com by Thu 3/21 5:00pm Pacific time. BIOE-PHD - Bioengineering (PhD) BMDS-MS - Biomedical Data Science (MS) BMDS-PHD - Biomedical Data Science (PhD) Date: Monday, March 11 2:00 PM – Wednesday, March 13, 2:00 PM Pacific Time Logistics: Administered on Gradescope 3 hours long (timer starts once you open the exam) Submitting answers (all questions visible at the same time): One PDF for the entire exam (uploaded at the top of the exam) One PDF for each question (uploaded to each question) Jure Leskovec is Professor of Computer Science at Stanford University. Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality Mining Massive Datasets (CS 246) 17 Documents. Mining Massive Final Exam Tentative plan: In-person exam during finals week Thursday March 20, 2025 from 12:15 – 3:15pm We will be releasing HW1 today It is due in 2 weeks (1/23 at 11:59 PM) Please start early. Regular Office Hours: We will have several in-person office hours per week. Stanford University. ) and cau Date: December 1, 1978 Place created: Stanford (Calif. I acknowledge and accept the Stanford Honor Code. 15. e. (a)Consider Fig. The emphasis will be on MapReduce and Spark as tools for creating parallel algorithms that can process very large amounts of data. large set. Uploaded by: Paul Caron. Video archive for CS246 Office Hours. CS 246: Problem Set 2 Tony Hyun Kim February 6, 2012 1 Recommendation systems 1. 1 Friend Recommender The source code of my FriendRecommend system can be found in the attachment to the assignment. • Familiarity with basic linear algebra (e. Shingling: Convert docs to sets of items Document is a set of k-shingles 2. Email the staff mailing list at cs246-spr2223-staff@lists. Combine: ideas from several ML models, e. As an example, for the following table A: A. Good knowledge of Java and Python For each node, enumerate over all features For each feature, sort the instances by feature value Use a linear scan to decide the best split along that 1/18/2024 Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246. 1 Proof by contrapositive Consider the statement \If it is Supplement to CS 246 providing additional material on the Apache Hadoop family of technologies. Traditional models allow you to encode prior knowledge better and give you more control. We denote the (i;j)th entry of a matrix Aas A ij, and the ith entry of a vector as v i. Q: How do I submit my assignment? A: Assignments (problem sets and all other reports) will be due at 11:59pm PST on Thursday. edu J ure Lesk ovec Stanf ord Univ ersity jure@cs . Stanford University; CS; CS 246; CS 246. More algorithms for streams: (1) Filtering a data stream: Bloom filters Select elements with property x from stream (2) Counting distinct elements:Flajolet-Martin Number of distinct elements in the last k elements of the stream (3) Estimating moments:AMS method Estimate std. Instructor: Jeff Ullman Office: 425 Gates Email: lastname @ gmail. ; GHW 2: Due on 1/21 at 11:59pm. Under no circumstances should you register for any of these services using your numerical SUID, 01234567. 1 shows the trajectory of stock #679 between 10 AM and 3 PM on Jan 5, 2006. Operated by cs-oh-queue-dev and CSD-CF at Stanford. 1 Vectors and vector operations A vector is a one dimensional matrix, and it can be written as a column vector: v = 2 6 6 6 4 v 1 v Compression set (CS): Groups of points that are close together but not close to any existing centroid These points are summarized, but not assigned to a cluster Retained set (RS): Isolated points waiting to be assigned to a compression set 1/21/2014 Jure Leskovec, Stanford C246: Mining Massive Datasets 35 1/19/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 2. 1/3/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 4 A large set of . 19. Interested in using this for your class? Fill out this form! Created by Carson Hoffman at University of Michigan. Stanford University Catalog . We often think of networks being organized into modules, clusters, communities: Jure Leskovec, Stanford CS246: Mining Massive Datasets 2 3 A large set of items e. Wi 2023. data Locality sensitive hashing Clustering Dimensional ity reduction Graph data PageRank, SimRank Network Analysis Spam Detection Infinite data The CS 246 Course Staff. ) CS 246: Mining Massive Data Sets | Problem Set 1 2 Questions 1 Spark (25 pts) [Hiroto, Kushaagra] Write a Spark program that implements a simple \People You Might Know" social network friendship recommendation algorithm. (a) What key-value pairs Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program (e. 2 2 quizzes. cs240-aut2425-staff@lists. all ticks where the trade price is not between the bid and ask prices. 1. , the things one customer buys on one day A general many-many mapping (association) between two kinds of things Stanford Office of Community Standards has more information. com by Tuesday, March 18 This combines best of machine learning, statistics, artificial intelligence, databases but more stress on Scalability (big data) Algorithms Computing architectures Automation for handling large data Cover Sheet Assignment Submission Fill in and include this cover sheet with each of your assign-ments. Assignments are due at 11:59pm. 86% (42) 15. Welcome to CS Office Hours! Select a course on the left to begin. Students can typeset or scan their homework (although we strongly recommend Predictive analytics, data mining and machine learning are tools giving us new methods for analyzing massive data sets. The matrix of these eigenvectors can be thought of as a 12 Clustering in two dimensions looks easy Clustering small amounts of data looks easy And in most cases, looks are not deceiving Many applications involve not 2, but 10 or 10,000 dimensions High-dimensional spaces look different: Almost all pairs of points are very far from each other --> The Curse of Dimensionality! 1/18/22 Jure Leskovec, Stanford CS246: Mining Massive Datasets CS 246 { Review of Proof Techniques and Probability 01/17/20 1. Hw1 - hw1. Winter 2019. 0 0 questions. , things sold in a supermarket A large set of baskets, each is a small subset of items e. Access to the Internet is permitted. Students will learn how to implement data mining algorithms using Hadoop and Apache Spark, how to implement and debug complex data mining and data transformations, and how to use two of the most popular big data SQL tools. edu to be added to Canvas. My algorithm relies on a chain of two Map-Reduce steps where: Map1: For a user iwith friends ff jg, emit all unique pairs (f j;f k) that are connected through user i Mining Massive Data Sets. Our training set comprises of the following features and labels: PatientID ˚ 1 Stanford students can see them here. Min-Hashing: Convert large sets into short signatures, while preserving similarity Familiarity with writing rigorous proofs (at a minimum at the level of CS 103). ; GHW 3: Due on 1/28 at 11:59pm. Deep models are often hard to scale and require lots of data. Shingling: Convert documents to large sets of items 2. Node features: Relational data: User/item descriptions, categories CS 246: Mining Massive Data Sets. The homework is long Requires proving theorems as well as coding We will also be releasing Colab 0 and Colab 1 CS 246: Mining Massive Data Sets - Problem Set 4 2 ˚ 2(x) = 1 if OGT test is positive, otherwise it is set to 0. Before submitting a complete application to Spark, you may go line line, checking the CS246 is a completion requirement for: . ˚ 3(x) = 1, a bias term Given a weight vector w= (w 1;w 2;w 3), our classi er returns +1 if w 1˚ 1(x) + w 2˚ 2(x) + w 3˚ 3(x) >0 and 1 otherwise. ) Familiarity with writing rigorous proofs (at a minimum, at the level of CS 103). He is affiliated with the Stanford AI Lab, Machine Learning Group and the Center for Research on Foundation Models. The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data. Office Hours. , copy and adapt the setup cells from Colab 0. zggu vrexynhq ikllumv xahmz aiaan vcmrs larpc lsiblhk kzp zrzkqptf