We wish to acknowledge that the land on which the University of Toronto operates has for thousands of years been the traditional land of the Huron-Wendat, the Seneca, and most recently, the Mississaugas of the Credit River and is still today the home to many Indigenous people from across Turtle Island; we are grateful to have the opportunity to work on this land.

Calendar (click on dates to jump to session information below)

MAY					JUN					JULY					AUGUST
Mo	Tu	We	Th	Fr	Mo	Tu	We	Th	Fr	Mo	Tu	We	Th	Fr	Mo	Tu	We	Th	Fr
3	4	5	6 welc	7		1	2	3	4				1	2	2	3	4	5 AIA	6
10 t ech	11	12	13 slow	14	7 log2	8	9	10 auto1	11	5	6	7	8	9	9 TBA	10	11	12 TBA	13
17 flow1	18	19	20 flow2	21	14 a uto2	15	16	17 i nfo	18	12	13	14	15 API	16	16	17	18	19	20
24	25	26	27 numb	28	21 repi	22	23	24 patt1	25	19 vis1	20	21	22 vis2	23	23	24	25	26	27
31 log1					28 patt2	29	30			26 aiml1	27	28	29 a iml2	30	30	31

KEY	class		exam due		holiday

Course Description

This course introduces fundamental concepts from computer science as critical thinking skills. These thinking skills can serve as a foundation for further studies in technology but they are useful in all manner of everyday problem solving.

Computational reasoning begins with the habit of "thinking slowly" and being deliberate about how we communicate solutions to problems. We learn the discipline of stepwise refinement and the power of abstraction and decomposition.

We build up procedural thinking starting with sequence - developing our "step by step" intuition - and then adding contingency - decisions - learning to pay attention to proverbial forks in the road - where are they? what are the alternatives? on what basis is the choice among them made? Sequence plus contingency allows us to add repetition to our repertoire. We learn to recognize the kinds of problems that can be solved iteratively and how to design iterative solutions. We will supplement these basic styles of thinking in hand we can begin to consider more complex problems by the method of decomposition - breaking a large, hard-to-solve problem into smaller, more manageable problem that we know how to solve - and by learning the art of shifting our focus up and down so that we can move between the details of a problem and its overall shape.

Those are the big ideas. More concretely, we'll learn about number systems and logic and the way we can represent these in digital and electronic form. We then turn our attention to the general problem of automation and unpack the basics of how automatic computational machines work. This will take us into the realm of data structures and algorithms, modularity and patterns.

And then we'll look at some applications asking how machines keep secrets, how machines see, how machines learn, and how to keep the behaviors of machines aligned with human values.

I will endeavor to make the pedagogy here "look, listen, do" - that is, start with the readings/viewings prior to each class session and then we talk about the ideas in class - mostly as lecture/demonstration but also some discussion, and then you practice the skills in the problem sets that follow each class session.

Problem sets will be implemented as Quercus quizzes, assignments, and discussions. In general there will be one problem set per course topic - most topics are one class session but some are multiple class sessions.

Basic Course Info

Instructor Information	Student Resources
Name: Dan Ryan Email: Via Piazza and Quercus Inbox. This term we will use Piazza for most back and forth between you and me and between one another about the course material. The system is designed to get you help fast and efficiently from classmates and myself. Rather than emailing questions, post them on Piazza. This way (1) everyone can see the answers and (2) a classmate might answer more quickly or more helpfully than I do. Find our class page at: https://piazza.com/utoronto.ca/summer2021/inf313h1ylec0101/home Virtual Office Hours: TBA. Always via appointment on the Quercus calendar. If no is signed up for office hours when I go to sleep the day before I reserve the right not to be there.	Quercus (Canvas) Student Guide iSchool Student Services Accessibility Services for iSchool Students Using Quercus Calendar

Instructor Information

Student Resources

Name: Dan Ryan

Email: Via Piazza and Quercus Inbox. This term we will use Piazza for most back and forth between you and me and between one another about the course material. The system is designed to get you help fast and efficiently from classmates and myself. Rather than emailing questions, post them on Piazza. This way (1) everyone can see the answers and (2) a classmate might answer more quickly or more helpfully than I do. Find our class page at: https://piazza.com/utoronto.ca/summer2021/inf313h1ylec0101/home

Virtual Office Hours: TBA. Always via appointment on the Quercus calendar. If no is signed up for office hours when I go to sleep the day before I reserve the right not to be there.

Quercus (Canvas) Student Guide

iSchool Student Services

Accessibility Services for iSchool Students

Using Quercus Calendar

SHORTCUTS

Course Schedule

The work for this course proceeds topic by topic. Some topics will be stretch across multiple sessions. Each topic consists of asynchronous preparatory work, a synchronous "lecture," and an asynchronous problem set. The problem set in the 4th, 8th, and 12th weeks will be comprehensive exams. The overall grade for the course will consist of 40% problem sets and 25% midterm and 35% final exam.

The exam problem sets will be similar to weekly problem sets but comprehensive (covering the whole course to date) and possibly longer than weekly ones. Previous exams are here. These will be completed asynchronously and will be "open book," but once you begin the exam you are expected not to consult with others.

1111	Welcome to the Course HW: post a one minute introduction video ZOOM \| notes \| slideshow \| pdf \| zip \| Problem Set \| Synchronous Recordings Readings, etc. Syllabus

0000

Our Tech Stack

Quercus (how to submit code as assignments, books, assignments, codepen, assessment, drawing tools (google drawing/slides; Miro; codepen; JS01; HTML01; Piazza, CSS01. HW: Quercus profile; Piazza post; codepen embed; Quercus office hour appointment; Submit as quercus quiz.

Readings, etc.

Piazza Introduction for Students
R3HAB MEDIA, "Intro to CodePen" (15m)

0001

Think Slowly

SLOW: stepwise refinement; pseudocode; abstraction/black box

Readings, etc. (about)

Ryan MiniLecture "Thinking Slowly" (2020) (9m36s)
Filho (2017) Computer Science Distilled, pp 1-5
EDC (2010), Harold "Doc" Edgerton
Kageyama (2012) Is Slow Practice Really Necessary? (3m)
Soni (2019) For creative solutions, slow and deliberate thinking is the key (3m)
Ryan MiniLecture "Stepwise Refinement" (2020) (9m36s)
Learn Learn Scratch Tutorials (2020), Stepwise Refinement Tutorial (7m)
CS2110 Cornell. (2019) Stepwise refinement: The Raven (6m)
LessonHacker (2013), Top Down / Modular Design (10m)
Khan Academy. n.d., Planning with pseudo-code | Becoming a better programmer (4m)
Codecademy (2019), What is Pseudocode And How Do You Use It? (11m)
Wikipedia. "Black Box" (5m)
OPTIONAL Cameron (2016), Writing Good Beginner Pseudocode (10m)

Interesting

Maneesh Agrawala, et al. "Designing Effective Step-By-Step Assembly Instructions"

0010

Flow + Modularity

FLOW I: flowchart conventions; pseudocode to flowchart; everyday language to flowchart;

FLOW II: principles of abstraction and modularity

Readings, etc.

Filho. 2017. Computer Science Distilled, pp 1-5
Khan Academy. n.d., Planning with pseudo-code | Becoming a better programmer 3m26s
Codecademy (2019), What is Pseudocode And How Do You Use It? (10m49s)
Ryan, D. "Flowcharts 101"
Systems Innovation. 2015. Modular Design (9m40s)

See also

Wikipedia Editors (n.d.), Flowchart
Wikipedia Editors (n.d.), Modularity

Interesting

Sarhangi R. nd. "Modularity in Medieval Persian Mosaics"

0011

Number Systems

numbers, exponents, logarithms. binary, powers of 2, dec2bin, bin2dec, arithmetic; hexadecimal, bin2hex, hex2bin, hex2dec, dec2hex; encoding ASCII, RGB, URL

Readings, etc.(Introduction to the Readings 4m9s)

Filho. 2017. Computer Science Distilled, p 165
Eames Labs. 1977. Powers of Ten (10m16s)
Sample from among
- MathCrazyTutoring (2007), Binary Numbers in 60 Seconds (1m35s)
- Khan Academy. 2011. "Binary Numbers" (10m33s)
- Khan Academy. 2014. "Introduction to number systems and binary" (9m59s)
- Cope (2017), Binary Numbers Explained for Beginners (14m25s)
Khan Academy. 2014. "Adding in Binary" (2m47s)
The Organic Chemistry Tutor. 2018. "How To Convert Binary to Hexadecimal" (10m13s)

FLIP

Ryan Intro Counting by Two (4m10s)

0100

Logic

LOGIC I: Logical values, and/or/not, truth tables, other operators; logic and binary, logic and electricity, simple logic circuits

LOGIC II: complex expressions, logical equivalence, DeMorgan's laws, simplification, Karnaugh maps, more complex circuits

Slides II (pdf | zip | live)

Readings, etc.

1. Filho. 2017. Computer Science Distilled, pp 5-13
2. INF1339. Bulls and Cows
3. Cheever (n.d.), "An Introduction to Digital Logic"
4. Sample these to learn about truth tables
  - Emily S. 2014. "Truth Table Tutorial" (7m50s)
  - Angiewvc. 2009. "Truth Table" (5m3s)
  - LearnYouSomeMath. 2016. "Truth Tables Tutorial (part 1)" (11m37s), Part II (10m35s)
  - William Spaniel has an explanation with lots (5m55s) of words on the screen
5. Shakeel, Y. 2016. "Basic Logic Gate - AND OR NOT" (Tutorial with 6m7s video)
6. James, J. 2014. "Boolean Algebra Explained part 1" (5m3s)
7. Sample these to learn about Karnaugh maps
  - The Organic Chemistry Tutor YouTube Channel (2019), Introduction to Karnaugh Maps - Combinational Logic Circuits, Functions, & Truth Tables
  - Computer Science YouTube Channel (2016), Karnaugh Maps – Introduction
8. Sample these to learn about 7-segment displays
  - Wikipedia Editors (nd), Seven-segment display
  - Watts, D. 2015. 4000 Series Logic ICs: The 4511 BCD 7 Segment Driver IC
  - ElectronicsTutorials (nd), 7-segment Display Counter Tutorial
  - Eater (2017), Designing a 7-segment hex decoder
  - dCode (nd), 7-Segment Display Decoder - Online Simulator Tool
See also
1. Electronics Tutorials series on binary, hex, octal numbers with connections to digital electronics
2. Secura, Roger. 2016. " THE BEGINNER’S GUIDE TO DIGITAL ELECTRONICS "
Interesting
1. Converters: bin2dec, dec2bin
2. Mrieppel. "Truth Table Generator"
3. Kim (2013), Logic in Alice in Wonderland (animated prezi)

EXAM	Midterm Problem Set I asdf

0101

Automation

AUTOMATION I: Machines, Automation, and Instructions, From Instructions to Goals

AUTOMATION II: Feedback & Systems, PID Controllers

Readings, etc.

1. Ryan, D. 2021 "Automation Intro 2021" (8m51s)
2. Ryan, D. 2020. "Automation Overview for INF313" (6m)
3. Wikipedia Editors. "Automation," "Governor"
4. Eames Office (1960), An Introduction to Feedback (10m48s)
5. Pangaro (2012), " What Is Cybernetics?" (5m16s)
6. Watch and listen to engineer guy Bill explain how music box mechanisms work (~8:00):
7. Douglas, D. 2015. "Why Learn Control Theory" (5m49s)
8. Sample to learn about open and closed loop automation
  - Science Channel. 2017. "Explaining Open and Closed loop Systems in Robotics - Control System Engineering" (5m4s)
  - Circuit globe (blog). 2019. "Difference Between Open Loop & Closed Loop System"
9. Sample to learn about causal loops
  - CLExchange (2016), Introduction to Causal Loops (3m49s)
  - Kirkwood (1997), System Behavior and Causal Loop Diagrams
10. Douglas (2015), PID Control - A brief introduction
See also
1. Control Solutions Minnesota (2019), PID for Dummies
2. Hitomi, K. 1994. " Automation — its concept and a short history." Technovation Volume 14, Issue 2, March 1994, Pages 121-128
Interesting
1. BBC Television (1950), Grey Walter's tortoises
2. @steam (2020), MCAS Simulation on Steam
3. Bennett (1984), Nicolas Minorsky and the Automatic Steering of Ships

0110

Info Jigs

From Information to Bits to Data, Primitive and Abstract Data Types, Stacks & Queues & Arrays, Lists, Maps, and Dictionaries, Your Friend JSON

Readings, etc.

1. DJR Lecture Part A (17m)
2. Filho pp. 65-83; 123-146; optionally 101-121
3. Haverbeke. 2019. "Values, Types, and Operators ," a chapter in Eloquent JavaScript.
4. Erwig (2017), The Search for the Perfect Data Structure ," a chapter in Once Upon an Algorithm: How Stories Explain Computing
5. Interview Cake. 2020. "Data Structures for Coding Interviews: Computer Science in Plain English "
6. Sample to learn something about JSON
7. codeSTACKr. 2019. "Learn JSON not Jason in 10 Minutes" (15m23s)
8. The Coding Train. 2015.10.2: What is JSON? Part I - p5.js Tutorial
9. The Coding Train YouTube Channel (2015), 10.3: What is JSON? Part II - p5.js Tutorial
Interesting
1. @theannalytical. 2020. "My Makeup Case as Data Structures"
2. Pokharel, R. 2020. "Data Structures and Algorithms in Everyday Life" (10m read)

0111

Repetition

Iteration Overview, Simple Repetition - Brute Force, Counting Repetitions, Recursion, nested loops; brute force; backtrack; recursion; memo-izing; orders of magnitude; orders of function (linear, quadratic, exponential); probability

Class 2: slideshow | pdf | zip

Readings, etc.

1. Filho ch 3 Strategy (pp 35-62)
2. Meyers, Bob @FSU Control Structures - Repetition
3. Learn some things about complexity
  1. Filho ch 2 "Complexity" (pp 25-34)
  2. Grace Hopper Academy. "Introduction to Computational Complexity - A Tutorial on Algorithms and Complexity" (13m36s)
  3. Hackerdashery. "P vs. NP and the Computational Complexity Zoo" (10m43s)
  4. Wikipedia. " Computational Complexity "
  5. Excellent Lecture about a Hard Topic by a University of Western Ontario Grad
    1. Borradaile, G. 2014. "Basics of Computational Complexity for Non-Computer Scientists" (37m47s)
  6. Drowell, E. "Know Thy Complexity"
  7. Schneider, C. n.d. "Introduction to Complexity Theory" (10pp)
See Also
1. Logarithms new to you?
  Hendy, T. 2019. Logarithms explained Bob Ross style
2. MDN Web Docs. 2020. "Loops and iteration (in JavaScript)"
3. Filho ch 5 Algorithms (pp 85-99)
Interesting
1. Guitar Tutor Online, "Repeat Signs"
2. Miley, J. 2018. "This College Professor Teaches Her Students Advanced Math Through Knitting"
3. The Open Book Project. " Iteration " from How to Think Like a Computer Scientist (longish - scan first)
4. Bernasconi,Anna, Chiara Bodei and Linda Pagli " On formal descriptions for knitting recursive patterns. ." Journal of Mathematics and the Arts Volume 2, 2008 - Issue 1, pp. 9-27

1000

Patterns

Patterns I: Generating Patterns, Recognizing Patterns, Exploiting Patterns

Patterns II: Regular Expressions

Readings, etc.

1. Bellos, A. 2019. "Can you solve it? On the tiles with the new Escher." The Guardian 11 Feb 2019.
2. mathscraftnz.org. "Escher Tiling Instructions"
3. Regular Expressions in Eloquent JavaScript
4. Work through: REGEXONE.COM's regular expression tutorial.
See Also
1. McCombe, Madeline. 2019. "How to use Regular Expression (Regex) and the Natural Language Toolkit (NLTK) on Game of Thrones Book 1"
2. Környei, Tibor. "Using MS Word’s Advanced Find and Replace Function"
3. Sartain, JD. 2016. "Word pro tips: Use Wildcards for faster, more accurate search-and-replace results"
4. Microsoft. "Split text into different columns with functions"
5. Wikipedia editors, "Regular Expression"
6. Regex.info. "Tutorial"

EXAM	Midterm Problem Set II Covers course material to date.

1001

APIs

concept, What/How; documentation; examples, requesting; processing;JS implementation, handling JSON

Readings, etc. (about)

1. DiCarlo Perre. 2016. "A Form Was Never Just a Sheet of Paper." Medium (15 min read)
2. MuleSoft Videos. 2015. "What is an API?" (3:24)
3. Berlind, David. 2015. "What are APIs and How Do They Work?" (5 parts, ~25 min read)
4. Rascia, Tanya. 2017. "How to Connect to an API with JavaScript" (15 min read, but longer to follow along and digest)
5. The Coding Train. 2015. "10.9: New York Times API and JavaScript - p5.js Tutorial" (15:18)
6. Ryan. "APIs in EDL," "APIs All Around Us," "Hydro as the Ultimate Abstraction," "Zotero API," "Who should know about APIs?" "The NYT API"
See Also
1. The Coding Train, "10.1: Introduction to Data and APIs in JavaScript - p5.js Tutorial" (12:49)
2. The Coding Train. 2015. "10.2: What is JSON? Part I - p5.js Tutorial"
3. The Coding Train. 2015. "10.3: What is JSON? Part II - p5.js Tutorial"
4. The Coding Train. 2019. "10.4: Loading JSON data from a URL (Asynchronous Callbacks!) - p5.js Tutorial"
5. TheCodingTrain.2019."1.5 Mapping Geolocation with Leaflet.js"
6. Harms, Johannes. 2013. "Research Goals for Evolving the ‘Form’ User Interface Metaphor towards more Interactivity" (3pp)
7. Bilner, Craig. 2018. "Alexa, Run This JavaScript App" (30 min read)
8. Muttoni, Andrea. 2018. "Building an Alexa Skill in 20 minutes using Node.js" (27m58s)
9. Alexa Developers. "Alexa Live How to Build Your First Great Alexa Skill" (44m14s)

How Do Machines Keep Secrets? (OPTIONAL EXTRA LECTURE)

history of encryption; math and application of RSA; implementation in secure web (https), hashing; fundamentals of blockchain

Readings, etc. (about)

1. A-beginners-guide-to-codebreaking-1.2.pdf"
2. Simple Substitution Cipher @ Practical Cryptography
3. Simply Explained. 2017. "Asymmetric encryption" (4m40s)
4. Khan Academy. 2015. "Encryption and public keys" (6m39s)
5. Goldstein, J. and D. Kestenbaum. 2010. "The Island Of Stone Money" NPR, Planet Money (4:24)
6. Anders Brownworth. 2016. Blockchain 101 - A Visual Demo (17:49)
7. Woo, E. 2014. "The RSA Encryption Algorithm 1 " (8m39s)
8. Woo, E. 2014. "The RSA Encryption Algorithm 2" (11m54s)
9. Nakamoto, Satoshi. "Bitcoin: A Peer-to-Peer Electronic Cash System" (8pp)
10. Anders Brownworth. 2017. "Blockchain 101 - Part 2 - Public / Private Keys & Signing" (8m53s)
See Also
1. Khan Academy: Bitcoin: Transaction records (11m30s)
2. Network Direction. 2019. "Encryption Basics | Cryptography" (12m58s)
3. PrivacyGuy. 2020. "What is Encryption and How Does It Work?" (6m28s) (or on Medium for a 5m read)
4. HardFork. "5 of the most ridiculous blockchain use cases, according to blockchain experts"
5. Ecocoin. Website.

1011

How Do Machines See?

VISION I: biological vision, neurons, perceptrons, weights, activation function

VISION II: network depth, diagram, tabular/data structure view, updates, convolutions

Readings, etc.

1. Harvey, J. 2015. "The evolution of the human eye" (4m43s)
2. Maini, Vishal. 2017. "Machine Learning for Humans: Why Machine Learning Matters" Medium (10 min read)
3. Khan Academy (2010), "Anatomy of a neuron" (6m12s)
4. Sharma, Sagar. 2017. "What the Hell is Perceptron?: The Fundamentals of Neural Networks" on Towards Data Science@Medium (3 min read)
5. Wikipedia. " Perceptron " (15 min read)
6. 3Blue1Brown. 2017. "But What is a Neural Network?" (19:13)
7. SimpleLearn.com. "Perceptron Tutorial" (20 min read, hour+ digest)
See Also
1. The Coding Train. 2017. "Neural Networks: Perceptron Part 1" (44:39)
2. Lagandula, Akshay. 2018. "Perceptron Learning Algorithm: A Graphical Explanation Of Why It Works." on Towards Data Science@Medium (8 min read)
3. Vasilev, Ivan. n.d. "A Deep Learning Tutorial: From Perceptrons to Deep Networks" (21 min read)
4. Maini, Vishal. 2017. "Supervised Learning (13 min read)
5. Maini, Vishal. 2017. "Supervised Learning II (10 min read)
6. Maini, Vishal. 2017. "Supervised Learning III (11 min read)
7. Klein, Dan. 2014. "Lecture 21: Machine Learning - Perceptrons." UC Berkeley. (more advanced) (1:14:18)

1100

How Do Machines Learn?

LEARNING I: sense of time and style diversity of ML/AI, where RL fits, decision trees, discounting, repetition in gridworld

LEARNING II: learning models

Asynch Lecture for Class II

Readings, etc.

1. 3Blue1Brown. 2017. "What is backpropagation really doing? | Chapter 3, Deep learning" (13m)
2. Sanchez, S G. 2021. "Visualizing Robert Frost’s Seminal Poem"
3. Guizzo, Erico. "What Is a Robot?" (IEEE) (15m read)
4. Owen-Hill, A. 2017. "What's the Difference Between Robotics and Artificial Intelligence?" (2m read)
5. Simon, Matt. 2017. " What is a Robot? " WIRED Magazine (10 minute video and text)
6. Wired. "If Robots Want to Work with Us, We Must Fix Four Problems" (5m8s)
7. Harrod, J.B. 2019. "What is Reinforcement Learning? | AI 101" (10m47s)
8. Maini, V. 2017. " Machine Learning for Humans, Part 5: Reinforcement Learning " (12m read)
9. Shyalika, C. 2019. "A Beginners Guide to Q-Learning" (10m read)
10. Wikipedia editors. "Q Learning" (15m read)
Recommended Watch
Our friend from PID controllers, Brian Douglas has a set of five video tutorials on RL. They're quite worth 75 minutes of your time.
See Also
1. Hadfield-Menell, D., A Dragan, P. Abbeel, S. Russell. 2017. " The Off-Switch Game " (Abstract, sections 1, 6, 7)
2. Wikipedia editors. " Markov Decision Processes "
3. Jones, T. 2017. "A Beginner's Guide to artificial intelligence, machine learning, and cognitive computing"

1101

Alignment

Nature of the problem. Modes of failure. Modes of governance.

Readings, etc.

1. Russell, S. 2015. "Value Alignment" (5m)
2. Russell, S. 2017. "Three Principles for Creating Safer AI." TED (17m24s).
3. Christian, B. 2020. Excerpt from The Alignment Problem (16pp)
4. "The Alignment Problem - In Conversation with Brian Christian and Nora Young" (57m)
5. Russell, S. 2019. "Technical Safety: Introduction to AI Safety"
6. Stop Autonomous Weapons. 2017. Slaughterbots (7:47)
Optional
1. Open AI. "Introducing Open AI"
2. Yudkowsky, E. 2016. "The AI Alignment Problem: Why It’s Hard, and Where to Start" (90m)
3. Larks. "2020 AI Alignment Literature Review and Charity Comparison"

EXAM	Final Problem Set Covers full course. Due 5pm 23 August 2021

Course Delivery Method

This is a lecture/problems/tutorial course that will be delivered remotely this semester. Some material will be available asynchronously and we will endeavor to make recordings of synchronous activity available but cannot guarantee that this will always be possible nor that it will always be accomplished promptly. Course related conversations will be handled via Piazza and all work will be submitted via Quercus.

E-mail Policy and Office Hours

Email: Correspondence that is inappropriate for Piazza should be done through Quercus Inbox. This ensures that it comes to me with a subject header that filters it into a mailbox that I pay attention to. I'm usually very prompt on emails. Exceptions late in the evening and on weekends.

Office Hours: TBA. Office hours are always via appointment on the Quercus calendar and held via Zoom If no is signed up for office hours when I go to sleep the day before I reserve the right not to show up.

Course Objectives

The purpose of this course is to convey, in a manner that is accessible to students coming from a wide variety of intellectual backgrounds and useful to students destined for a wide variety of further avenues of study, a set of intellectual reflexes and styles of thought and a repertoire of practical skills drawn from, and deployed in, computational science. The course is NOT intended as "computer science 101" or "a first course in programming" or "introduction to data processing."

Course Learning Outcomes

This course introduces students to a style of thinking that yields problem solutions that can be programmed, are predictably reliable, involve careful and exhaustive consideration of problem and solution spaces, and can be implemented at high speed and high volume. Although this usually means solutions that are carried out by machines, the application of this style of thinking is more generally applicable.

The goal, then, will be to inculcate an "algorithmic" style of thinking: the deliberate decomposition of large and complex problems into a set of nested subproblems, the construction of solutions in terms of sequence, repetition, and contingency, the recognition and exploitation of pattern, and the inclination to defer detail, automate, and modularize.

Upon successful completion of this course you will have enhanced your capacity to

have enhanced general purpose critical thinking skills (THINK)
think slowly about things that move fast and/or have many parts (SLOW)
break large and complex problems into cognitively/computationally/organizationally manageable subproblems (DECOMPOSITION)
recognize, describe, and exploit patterns (PATTERN)
defer detail and move between levels of abstraction (ABSTRACTION)
build complex, robust, and legible systems from cognitively manageable and reusable parts (MODULARITY)
think about data and information structurally (DATA)
understand the complexity of computational solutions (COMPLEXITY)
formulate problems and solutions using computational thinking in different fields of endeavor (APPLY)
deploy a set of mental categories, intellectual reflexes, conceptual tools, and practical skills necessary for a participatory role contemporary information technologies (TECH)

Calendar (click on dates to jump to session information below)

Course Description

Basic Course Info

Course Schedule

1111

Welcome to the Course

Readings, etc.

0000

Our Tech Stack

Readings, etc.

0001

Think Slowly

Readings, etc. (about)

Interesting

0010

Flow + Modularity

Readings, etc.

0011

Number Systems

Readings, etc.(Introduction to the Readings 4m9s)

FLIP

0100

Logic

Readings, etc.

EXAM

Midterm Problem Set I

0101

Automation

Readings, etc.

0110

Info Jigs

Readings, etc.

0111

Repetition

Readings, etc.

1000

Patterns

Readings, etc.

EXAM

Midterm Problem Set II

1001

APIs

Readings, etc. (about)

How Do Machines Keep Secrets? (OPTIONAL EXTRA LECTURE)

Readings, etc. (about)

1011

How Do Machines See?

Readings, etc.

1100

How Do Machines Learn?

Readings, etc.

1101

Alignment

Readings, etc.

EXAM

Final Problem Set

Course Delivery Method

E-mail Policy and Office Hours

Course Objectives

Course Learning Outcomes