COURSE INFORMATION
Instructor	Paul Skokowski Symbolic Systems & Philosophy paulsko office: Philosophy Bldg 100, Room 101G office hours: Thurs 4:30-5:30pm & by appointment. Professor Skokowski is happy to see any student for any reason during office hours. Note: Please do not email the instructor except in case of emergency. First contact should be with your TA, or for general inquiries use Piazza. Also be mindful when using Piazza to not post explicit answers to questions in the problem sets, or, whenever necessary, do so as a "private" post that is only visible to the TAs and course instructor. Piazza link: piazza.com/stanford/fall2017/symsys1
Teaching Assistants	Sara Altman, skaltman office hours: Thurs 2-3:20pm location: 460-040E Andres Camperi, camperia office hours: Thurs 7-8:30pm location: 460-040E Dirk Hofland, dhofland office hours: Weds 2-3:20pm location: 460-040E Gerardo Rendon Gonzalez, grendon office hours: Wed 10-11:30am location: 460-040E Rolfo Mathieu, rolfom01 office hours: Mon 3-4:20pm location: 460-040E George Supaniratisai, sspkpl office hours: Thurs 12am-1:20pm location: 460-040E Brendan Fleig-Goldstein, bfleig office hours: Tues 1:45-3:15pm location: 460-040E Sonia Targ, sktarg office hours: Fri 3-4:20pm location: 460-040E Da Eun Kim, daeunk office hours: Thurs 4:30-6pm location: 460-040E Pedro Garzon, pgarzon office hours: Weds 3-4:20pm location: 460-040E Timothy Wu, thsuanwu office hours: Tues 3:15-4:30pm location: 460-040E Amartya Das, adas17 office hours: Fri 1:30-2:50 location: 460-040E Note: office hours begin the second week of classes (from 10/2/17)
Meeting times	Tuesdays and Thursdays 10:30-11:50 am, starting September 26, 2017 plus sections (beginning week 2)
Location	Cubberly Auditorium
Description	An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Undergraduates considering a major in Symbolic Systems should take this course as early as possible in their program of study.
Contact information	For most course-related inquiries, your first point of contact will be your teaching assistant. For general inquiries use Piazza. Also be mindful when using Piazza to not post explicit answers to questions in the problem sets, or, whenever necessary, do so as a "private" post that is only visible to the TAs and course instructor. Piazza link: piazza.com/stanford/fall2017/symsys1. Except in case of emergency or sensitive personal issues, do not email the instructor directly.
	Beginning with the Class of 2018, students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites.
Assignments	There will be weekly assignments, starting the second Friday (October 6) and due every Friday thereafter. All assignments will be submitted via Canvas. See the assignment schedule on Canvas for details.
Sections	Sections meet every week beginning the second week of class, on Monday October 3. They will cover the material presented in the previous week's lectures. Section attendance constitutes 9% of your final grade (1% per section. Important! Only sign up for Sections on CANVAS! (Do NOT sign up for sections on Axess!) Signup window for sections on CANVAS will be announced sometime during 1st week.
Readings	Students should do all assigned readings in advance of the class for which they are assigned. This is crucial for success in the class, and lectures will assume prior familiarity with the contents of the readings. The readings will be drawn from two sources: Danny Hillis, The Pattern on the Stone, 2nd edition, available in the Stanford Bookstore (and the usual alternative sources) IMPORTANT!!! Course Reader must be ordered ONLINE from: Copy Central Berkeley Price: $48 hardcopy (includes UPS shipping), $40 digital rental (read only, not printable, 180 days), or BOTH together for $74.79. Hardcopies will arrive in 2-3 business days. Again, ORDER READER HERE: https://copycentral.redshelf.com/book/804038/symsys-1-minds-and-machines-804038-none-skokowski I would recommend ordering the reader before class begins, for obvious reasons.
Course contract: electronics in class	By enrolling in "Minds & Machines", you are signing up for the following contract: No laptop computers, smartphones, iPads, or other internet-enabled devices during class meetings. Students should bring a notebook/notepad and pen/pencil to class for note-taking purposes, as well as the course reader or Hillis book as applicable. This contract has been created in response to a large body of educational research demonstrating that laptop and phone use in class is detrimental to learning. We will discuss this research further in the first course meeting.
Grading	The breakdown will be as follows: Weekly assignments: 65% Take-home final: 26% Section attendance: 9% Note that there is no midterm. The final exam will be released the last day of class, December 7, at 5PM. It will be due exactly one week later, on December 14 at 5PM. No late finals will be accepted. Late policy. Every student gets two penalty-free late days total for the quarter (Weekly assignments only, not the Final), where a day is charged for lateness between 0 and 24 hours after the time the assignment is due. After the two late days are used, a penalty of 25% per day off of the total score will be charged, based on the timestamp of the submitted assignment, e.g. an assignment with a score of 100 turned in 24 hours and 1 minute late after both free late days have been used up will be given a score of 50 for grading purposes.
Students with Documented Disabilities	Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE).  Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty dated in the current quarter in which the request is being made. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations.  The OAE is located at 563 Salvatierra Walk (phone: 723-1066, URL: http://oae.stanford.edu).
Collaboration & plagiarism policy	All work submitted should be exclusively your own, unless you have explicitly been assigned to a group for a specific project. You may discuss homework questions verbally, but you may not share any written documents pertaining to homework questions, including emails, draft answers, etc. You should also consult Stanford's plagiarism policy carefully. If you use ideas from someone else, you should cite a source. If you use someone else's words, you should indicate this by using a quotation and citing a source. Failure to follow the plagiarism policy is a serious offense and can lead to major sanctions, including failing the class and official sanctions through the Office of Community Standards.
Preliminary Schedule (There Will Be Changes!)	Date Guest? Topic Reading Extras Lecture Media Tues. 9/26 Thinking about Intelligence Human-Level Artificial Intelligence? Be Serious! (all) (fun) The Robots are Winning (serious) Laptops in class: [one] [two] [three] [four] etc. etc. [slides] Thurs. 9/28 Bodies, Minds, and Machines I   Sel. from "Minds and Bodies" (all) "Meat machines" (7-15; 25-27) Leibniz: The Nature and Communication of Substances CANVAS: On the hypothesis that animals are automata [slides] Tues. 10/3 Bodies, Minds and Machines II CANVAS: On the hypothesis that animals are automata Wittgenstein CANVAS: Sels. from Place and Smart CANVAS: Sel. from Ryle [slides] Thurs. 10/5 From embodied to abstract machines ch.1-2 and ch.4 of The Pattern on the Stone (1-38; 61-76) ch.3 strongly recommended if you don't know how to program Tinker Toy computer Lego Turing Machine Scooping the loop snooper [slides] Tues. 10/10 Machines for natural language grammars "How language works" (83-103) [slides] Thurs. 10/12 Brains as machines? Sel. from Computing the Mind (26-44) "Chinese room argument" (all) Mapping communication networks in the brain [slides] Tues. 10/17 Rules & representations, levels of analysis "Rules & representations" (all) Marr, sel. from Vision (all) Can Neuroscience Understand Donkey Kong, Let Alone a Brain? From circuits to behavior: A bridge too far? [slides] Thurs. 10/19 Noah Goodman The probabilistic language of thought "Language of thought" (all) "4 Problems Solved by the Probabilistic Language of Thought" (all) Helpful probability notes [slides] Tues. 10/24 Representation and computation in animal & human cognition "Learning & representation" (all) Donahoe review of Gallistel & King book Gallistel response to Donahoe [slides] Thurs. 10/26 Brian Knutson Decision and the brain "When brain beats behavior: Neuroforecasting crowdfunding outcomes" (all) Background: "Neuroscience, Psychology, and Economic Behavior" [slides] Tues. 10/31 Hyowon Gweon Social cognition & theory of mind "Universal social cognition" (all) [slides] Thurs. 11/2 Bayesian inference, Realism and Anti-Realism "Bayesian models of cognition: What's built in after all?" (all) How machines learned to think statistically CANVAS: Arguments concerning scientific realism (van Fraassen) [slides] Tues. 11/7 Neural networks intro Pattern on the Stone, ch.8 (all) Marcus, "Multilayer perceptrons" (7-20; 25-34) Sunspring Neural network playground The Unreasonable Effectiveness of Recurrent Neural Networks CANVAS: Arguments concerning scientific realism (van Fraassen) [slides] Thurs. 11/9 Justin Gardner Vision and the brain "Bayes' rule in perception action and cognition" (all) ch.1 of Basic Vision (all) [slides] Tues. 11/14 Jay McClelland Parallel distributed processing "Parallel distributed processing at 25" (1024-1046) [slides] Tues. 11/16 Philosophy and Neuroscience Selections from Dennett, Churchland, Gold & Stoljar P. Churchland [slides] Tues. 11/28 Andrej Karpathy Deep learning "Deep machine learning" (all) Intro background: LeCun, "Machines who learn" Advanced: LeCun et al., "Deep learning" [slides] Thurs. 11/30 Bill Newsome Neuroscience: the Path Forward Can a human brain understand itself? "The BRAIN Initiative: developing technology to catalyse neuroscience discovery" [slides] Tues. 12/5 Bats, zombies, and qualia CANVAS: Nagel, Tye, Jackson [slides] Thurs. 12/7 Review & discussion (take-home final released 5PM)