Introduction to Stanford A.I. If your name or pronouns Please use Piazza for all questions related to lectures and coursework. (worth bonus points: might help if you are in the middle between grades). Students are expected to have background in basic probability theory, statistics, programming, algorithm design and analysis. ideas in discussion as you would wish. taking unusual and unreasonable precautions to prevent the forms of dishonesty mentioned above. The Honor Code is the university's statement on academic integrity written by students in 1921. We will focus on problems such as transitive closure, shortest paths, graph matching, and many other classical graph problems. Focusing on a variety of graph problems, we will explore topics such as small space graph data structures, approximation algorithms, dynamic algorithms, and algorithms for special graph classes. For Enterprise For Students. accommodations, and prepare an Accommodation Letter for faculty dated in the current quarter in which the request is being Each late homework should be clearly marked as “Late” on the first page. 3 Units. For a particular homework, you can use only two late days. Courses were recorded during the Fall of 2019 CS229: Machine Learning Video Course Speaker EE364A – Convex Optimization I John Duchi CS234 – Reinforcement Learning Emma Brunskill CS221 – Artificial Intelligence: Principles and Techniques Reed Preisent CS228 – Probabilistic Graphical Models / […] Midterm extension: the midterm is now due Nov 3 at 11:59pm. Anybody violating the honor code will be referred to the Office of Community Standards. Teach the basic results in number theory, logic, combinatorics, and graph theory. Graph Theory and Combinatorics Topics in combinatorial mathematics that find frequent application in computer science, engineering, and general applied mathematics. http://isites.harvard.edu/course/colgsas-6411 Extra Credit (+3%): You will be awarded with up to 3% extra credit if you answer other students’ questions on Piazza in a substantial and helpful way, or contribute to the course notes on GitHub with pull requests. Class hours: MW 11:00am-12:15pm, 380-380Y change during the course, we invite you to share this with us and/or other students, so we may talk with you and refer to your others as well as themselves uphold the spirit and letter of the Honor Code. Topics include number theory, set theory, logic, graph theory, and combinatorics. While the faculty alone has the right and obligation to set academic requirements, the students and faculty will work together to This course is an introduction to advanced topics in graph algorithms. News flashes: 11/26/19 - Marc Levoy's team has published a new article in the Google Research Blog about astrophotography on Pixel 4.; 10/28/19 - Marc Levoy's team has open-sourced an API for retrieving dual-pixel data from recent Pixel phones. (25% of the grade), We have some scribed lecture notes from previous years. Graphical models bring together graph theory and probability theory, and provide a flexible framework for modeling large collections of random variables with complex interactions. Student contributions welcome! You should not copy, refer to, or look at the solutions from previous years’ homeworks in preparing your answers. Topics include: basic notions, connectivity, cycles, matchings, planar graphs, graph coloring, matrix-tree theorem, conditions for hamiltonicity, Kuratowski's theorem, Ramsey and Turan-type theorem. If you don’t want to share a set of pronouns for yourself, that is perfectly acceptable, too. There are many software packages available that can greatly simplify the use of graphical models. Your job would be to edit at least one lecture, improving and updating the previous version, and submit the revised LaTeX notes within a week of the lecture. You are encouraged to use LaTeX to writeup your homeworks (here is a template), but this is not a requirement. Good familiarity with algorithmic concepts is necessary. The faculty will also avoid, as This is due to people getting the midterm in their spam folders... 9/27: Algorithms for subgraph isomorphism: Finding copies of a pattern in a large graph, 10/11: Hitting Set Arguments, All Pairs Shortest Paths, 10/13: More APSP; Graph Spanners; HW1 due, HW2 out, 10/24: HW2 due; 10/25: Take Home Midterm out (by email), 10/27: Finish compact routing; Dynamic Algorithms, 11/1: Take Home Midterm Due (by email); HW3 out, 11/1: Dynamic Algorithms (Graph Connectivity); Paper List Out: pick a paper first come, first serve, 11/3: Dynamic Algorithms (Transitive Closure), 11/8*: Dynamic Algorithms (Graph Matching), 11/10*: Treewidth, Planar graph algorithms, Last two weeks (11/29,12/1,12/6,12/8): presentations. The course will cover: (1) Bayesian networks, undirected graphical models and their temporal extensions; (2) exact and approximate inference methods; (3) estimation of the parameters and the structure of graphical models. Department of Mathematics Building 380, Stanford, California 94305 Phone: (650) 725-6284 Email Graphical models bring together graph theory and probability theory, and provide a flexible framework for modeling large collections of random variables with complex interactions. News flashes: 12/1/14 - New Stanford faculty member Gordon Wetzstein will be teaching CS 448I, Computational Imaging and Display, in Winter quarter. Time: Fridays 12:30pm--3:20pm, Building 460, Room 334. Graphical models bring together graph theory and probability theory, and provide a flexible framework for modeling large collections of random variables with complex interactions. Courses in the program teach students to create, analyze, and interpret mathematical models and to communicate sound arguments based on mathematical reasoning and careful data analysis. 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