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CS371: Introduction to Cognitive Science

Bryn Mawr College, Fall 2016
Department of Computer Science
Professor Douglas Blank

1. Overview¶

Cognitive science is the interdisciplinary study of intelligence in mechanical and organic systems. In this introductory course, we examine many topics from computer science, linguistics, neuroscience, mathematics, philosophy and psychology. Can a computer be intelligent? How do neurons give rise to thinking? What is consciousness? These are some of the questions we will examine. No prior knowledge or experience with any of the subfields is assumed or necessary. It is assumed that you have junior or senior ranking in a field related to cognitive science. Otherwise, you should have permission of the instructor.

Big questions we will explore this semester:

can a computer think?
what is consciousness?
can a computer be conscious?
can a computer program do something it was not programmed to do?

1.1 General information:¶

Course website: http://cs.brynmawr.edu/cs371
Class: Monday and Wednesdays, 11:40am-1:00pm, Park Science Building, Room 336
Lab: Thursdays 2:30pm-3:20pm, Park Science Building, Room 230
Professor: Douglas Blank, http://cs.brynmawr.edu/~dblank/
Email: dblank@cs.brynmawr.edu
Office: Park Science, Room 248
Office phone: (610)526-6501
Office hours: Tuesdays 10am-noon
Course Management System: https://moodle.brynmawr.edu/course/view.php?id=3232

2. Resources¶

We will use the following resources for this course:

Jupyter on Athena at Bryn Mawr College: https://athena.brynmawr.edu
Handouts and outside readings that will be distributed in class and through Moodle
All of the notebooks for this course

2.1 Video resources¶

https://www.youtube.com/watch?v=TryOC83PH1g

3. Schedule¶

3.1 Calendar¶

      October               November              December        
Su Mo Tu We Th Fr Sa  Su Mo Tu We Th Fr Sa  Su Mo Tu We Th Fr Sa  
                   1         1  2  3  4  5               1  2  3  
 2  3  4  5  6  7  8   6  7  8  9 10 11 12   4  5  6  7  8  9 10  
 9 10 11 12 13 14 15  13 14 15 16 17 18 19  11 12 13 14 15 16 17  
16 17 18 19 20 21 22  20 21 22 23 24 25 26  18 19 20 21 22 23 24  
23 24 25 26 27 28 29  27 28 29 30           25 26 27 28 29 30 31  
30 31

3.2 Weekly Plan¶

Date	Topic
Week 1
Aug 29, Mon	Introductions, and Tools
Aug 31, Wed	Python, Artificial Intelligence, and Symbolic Reasoning; assign teams for Lab02. Perception.
Sep 1, Thu	Lab: Lab01: Getting Started
Week 2
Sep 5, Mon	Labor Day: No classes
Sep 7, Wed	Discuss Chess and what is intelligence? Chess due Tuesday Sep 13, 5pm. Today's notes
Sep 8, Thu	Lab: Lab02: Playing Chess, see Programming a Chess Player
Week 3
Sep 12, Mon	Discuss Computing Machinery and Intelligence or alternative version.
Sep 14, Wed	Chess Tournament Results, Presentation, and Discussion. Discuss The Chinese Room.
Sep 15, Thu	Lab: No lab meeting (Doug out of town); Lab03: Deep Learning, assigned
Week 4
Sep 19, Mon	Discuss connectionism.
Sep 21, Wed	Neural Networks: memory, activations, long-term/short-term memory. Representing Time in Connectionist Networks
Sep 22, Thu	Lab: Lab04: Predicting Text
Week 5
Sep 26, Mon	Neural Networks. Generalization. How to make a plot
Sep 28, Wed	Neural Networks. Discuss Finding Structure in Time [2]
Sep 29, Thu	Lab: Lab05: Finding Structure in Time: Parallel Networks that learn to Pronounce English Text [3]
Week 6
Oct 3, Mon	Recurrent Neural Networks. Jordan Networks, Simple Recurrent Networks, LSTM. Note: Oct 2 premiere of Westworld.
Oct 5, Wed	Discuss "Finding Structure in Time". Clustering, Principal Component Analysis
Oct 6, Thu	Lab: Lab06: Evolving Brains
Week 7: Fall Break
Oct 10, Mon	No class
Oct 12, Wed	No class
Oct 13, Thu	Lab: No lab meeting
Week 8
Oct 17, Mon	Introduction to Robots
Oct 19, Wed	Evolving Robots; Project ideas: attention, memory, analogy, decisions, etc.
Oct 20, Thu	Lab: Lab07: Project Proposal
Week 9
Oct 24, Mon	Analogy and Perception; Project Proposals due
Oct 26, Wed	Search
Oct 27, Thu	Lab: Lab08: work on project
Week 10
Oct 31, Mon	Perception and Analogy. Read High-Level Perception, Representation, and Analogy: A Critique of Artificial Intelligence Methodology
Nov 2, Wed	Simple Logistic Regression, notes
Nov 3, Thu	Lab: Lab09: work on project
Week 11
Nov 7, Mon	Philosophy Experiments, discussion of ethics, morals, and AI
Nov 9, Wed	Work on project (day after election)
Nov 10, Thu	Lab: Lab010: work on project
Week 12
Nov 14, Mon	An Emergent Framework for Self-Motivation in Developmental Robotics
Nov 16, Wed	Robotics
Nov 17, Thu	Lab: Lab11: work on project
Week 13
Nov 21, Mon	Evolving Communication
Nov 23, Wed	No class
Nov 24, Thu	Lab: No lab meeting
Week 14
Nov 28, Mon	Wrapping up
Nov 30, Wed	Project Presentations
Dec 1, Thu	Lab: No lab meeting
Week 15
Dec 5, Mon	Project Presentations
Dec 7, Wed	Project Presentations
Dec 8, Thu	Lab: No lab meeting

3.3 Important Dates¶

Classes Begin (Bryn Mawr & Haverford), Monday, August 29, 2016
Labor Day: No Classes, Monday, September 5, 2016
Registration Ends, Wednesday, September 7, 2016
Fall Break begins after last class, Friday, October 7, 2016
CR/NC Sign Up Deadline (Semester Long Courses), Friday, October 7, 2016
Fall break ends, Sunday, October 16, 2016
Spring 2017 Preregistration Begins, Monday, November 14, 2016
Spring 2017 Preregistration Ends, Friday, November 18, 2016
Thanksgiving Break (begins after last class), Wednesday, November 23, 2016
Thanksgiving Break ends, Sunday, November 27, 2016
Last Day of Classes, Thursday, December 8, 2016
Reading Day, Friday, December 9, 2016
Self Scheduled Exams Begin, Saturday, December 10, 2016
Exams End at 12:30pm, Friday, December 16, 2016
Fall 2016 Final Grade due (by noon), Tuesday, January 3, 2017

4. Grading¶

Final grades will be calculated as a weighted average of all grades according to the following weights:

Labs: 60%
Participation: 20%
Project: 20%

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