CS 477: Artificial Intelligence And Machine Learning

Ursinus College, Fall 2023

Course Evolving: Site Last Updated 12/6/2023

Table Of Contents

Overview Technology Logistics Readings

Deliverables Schedule Grading

Classroom Environment Collaboration Policy Other Resources / Policies

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Overview

Class Times / Locations

• Monday/Wednesday/Friday, 2:30-3:20PM in Pfahler Hall Room 107

Student Office Hours

• Monday/Wednesday 3:30PM - 5PM, Friday 3:30 - 4:30PM Pfahler 107
• Tuesday evening 7PM - 9PM over zoom in 20 minute blocks, by appointment

Prerequisites/Requirements

Math 111 or equivalent and CS 274 or CS 275, or permission of instructor

Instructor

Christopher J. Tralie

I grew up right around the corner in the Montgomery County and attended Upper Dublin High school (class of 2007). I then did my undergraduate degree in Electrical Engineering at Princeton University and my master's and Ph.D. degrees in Electrical And Computer Engineering at Duke University (heavily studying math and CS along the way). I finally started my dream job at Ursinus College in Fall of 2019! You can read more about my interests on my professional web site. Looking forward to getting to know everyone as we work through this course together!

Course Description

This capstone course will give students a broad overview of both AI and machine learning at an undergraduate level, and it will act as a primer for future graduate work or industry work in these areas. This is a daunting task, as we are currently in an "AI summer," where progress is moving extremely rapidly. As such, we will strive to strike a balance between surveying important modern developments, while also developing a strong classical foundation and conceptual framework for modeling and solving problems. We will take tools from algorithms and data structures, statistics, and numerical math to build strategies for knowledge representation, reasoning, planning, state space search, and supervised, unsupservised, and representation learning. Armed with these tools, we will examine applications in game playing, natural language processing, image processing, computer vision, and digital audio. We will be ever mindful of the ethical implications of AI/ML as we build this knowledge, and five classes will be devoted to studying ethical issues. Finally, the course will culminate in an open ended ethics project in which students do a deep dive into a contemporary ethics issue in artificial intelligence.

Learning Goals

1. Develop broad knowledge of classical AI and modern machine learning techniques and the foundation to learn any topic in modern AI after the course.
2. Learn how to incrementally design, implement, and test engineering pipelines in code.
3. Determine the strength and limitations of different algorithms from both and engineering and ethical standpoint.
4. Develop critical thinking skills and careful consideration around the hype surrounding AI and the abuse of AI in contemporary society.

Learning Objectives

1. Apply classical and modern algorithms in supervised learning, unsupervised learning, and reinforcement learning.
2. Learn basic data wrangling techniques.
3. Develop fluency with abstract modeling of problems as state spaces and geometric landscapes.
4. Practice patient problem solving by developing comfort with the edit -> compile -> run loop, along with intermediate debugging skills.

Core Quest

This class will address one of the open questions in the Ursinus core quest: How do we live together?

Automated systems are now deployed in every facet of our lives, from entertainment and recommendation systems, to hiring decisions, to healthcare, to transportation, to policing, and beyond. This can lead to a power asymmetry in our contemporary society between those who have CS knowledge and those who don't, as a few elite individuals with specialized knowledge may effectively govern these realms without a mandate. This has lead to widespread harm in systems that are deployed without proper oversight or transparency, particularly because of the scale at which these systems operate and the speed at which they are deployed and financed with very little oversight or input from the public.

Though the primary focus of CS 477 is on technical details of the algorithms behind AI systems, students simultaneously study the harm that some of these systems pose and have historically posed, and they develop critical reasoning via a sustained series of readings and discussions throughout the course on AI ethics. This is such an important topic that it's difficult to do it justice, but we will be focusing on the following five topics, each of which will have a class devoted to it:

1. An overview of AI ethics (social networks, recommendation systems, intentional and unintentional bias in data driven systems)
2. Colonialism in AI / the cost of data labeling
3. Climate change and the environmental costs of AI
4. On The Dangers of Stochastic Parrots and large language models like ChatGPT
5. Intellectual property and AI: data scraping, artist consent and implied inevitability

Beyond that, most programming assignments have graded reflections on the potential harms of that code, and students do a final research paper on an ethics topic of interest to them.

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Technology Logistics

We will be using a zoo of technologies in the course, as has become standard in 21st century work environments. Below is a table summarizing what kinds of communications/activities occur via each technology, and below that there are more details on everything. This is admittedly complex, and it will take some getting used to, but it will be worth it once we get it nailed down.

NOTE: I will repeat the same announcements across e-mail and Discord, so you don't have to check all both for announcements.

Class web site (You are here!)	Schedule (Topics, Study Resource Links, Assignment Deadlines) Assignment descriptions
Canvas	Assignment Deadlines, Submission, and Feedback
Discord	Chat/polls during class Announcements Sending code during student office hours Class-wise questions and discussions, including anonymous questions Individual chat with Prof. Tralie of a less sensitive nature (e.g. tech support, assignment questions not appropriate for the entire class)
Zoom	Tuesday night remote office hours, by appointment
E-mail	Announcements Individual chat with Prof. Tralie of a more sensitive/private nature (e.g. questions about grades, academic planning, personal issues) Individual chat with Prof. Tralie of a less sensitive nature (e.g. tech support, assignment questions not appropriate for the entire class)

*: For privacy reasons, anything of a personal nature, and particularly things that have to with educational records (e.g. grades), need to be kept within Ursinus sanctioned platforms like Outlook e-mail.

Canvas

We will be using Canvas, but only to submit assignments and to store all of the grades. I will also keep all of the due dates current on the calendar there, as students have appreciated this common space for all of their classes in the past.

Discord / Anonymous Questions

To facilitate informal, class-wide discussions about the class, as well as buddy group coding with screen sharing, we will have a Discord channel for the class. My goal is for this to turn into a flourishing area to work through confusion and to share ideas as a group.

Voltaire Anonymous Questions

All questions are welcome! To help break down the barrier of asking questions, we will be using the chat bot Voltaire so students can ask questions anonymously. This has worked very well in the past. To use Voltaire, send a direct message to the Voltaire bot with the following syntax:

where channel_name is the name of the channel you want to post to, and message is the text you want to send. Below is an example:

Answer:

In Class Anonymous Questions

In addition to Voltaire, I'll be running a bot during class that accepts questions to help quieter student who want to participate anonymously to do so. I've found this sort of thing to be particularly helpful with students from underrepresented groups. To ask a question during class, visit http://mathcs.ursinus.edu/question.

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Readings

Because we are drawing from such a wide array of topics in both artificial intelligence and machine learning, some of which are incredibly recent, there is no official textbook for the technical part of the course. Instead, readings from a variety of sources and notes that I've written will be linked on the schedule.

Beyond that, click here to see a list of my recommendations of books on AI and society.

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Deliverables

Class Engagement / Pre-Class Work

Since the material from this class is so different from an ordinary CS class, I want students to have thought about it a bit before coming to class so we can answer deeper questions and do more interesting exercises. Therefore, I will be following a largely flipped model with 2-3 modules per week that walk you through concepts with videos I made, followed by autograded python/numpy exercises. I worked hard on these, so I hope you enjoy them, and I hope it's an opportunity for you to study at your own pace!

NOTE: These modules are meant to be low stakes compared to the assignments, so please reach out to me or post on discord to the rest of the class if you get stuck on anything. I'm looking forward to some lively discussions as students work through them!

Beyond that, we will have 5 classes devoted to ethics discussions. I will ask you to watch a video or to do some readings before class, and to answer a few brief reflection questions to make sure you are prepared to have a meaningful discussion. I will also grade for participation during these classes.

Programming Assignments

The bulk of the grade in the course will be earned by completing 10 small to medium scale programming assignments. Be sure to start them early! Note that collaboration and sharing rules differ slightly between the individual assignments and the large assignments.

NOTE: I will not grade assignments that do not run! So be sure to hand in code that runs. Send me or the class messages ASAP when you run into syntax errors you can't resolve on your own.

Debugging Principles

If you're taking this course, then you've certainly had experience with debugging, but it is a skill you will still need to work on, so you should expect to hit some roadblocks. In fact, it is time consuming and difficult even for very experienced programmers. So do not be hard on yourself if your programs don't work the first time around (they rarely do, even if you've been programming for decades!).

I have had over 20 years of programming experience at this point, and I have learned the hard way what works and what doesn't. Here are my main debugging principles in a nutshell

1. Leave yourself adequate time to work on the assignments, because the amount of time it takes to resolve issues can be unpredictable.
2. Write small bits of code and test them right away. Don't write a wall of code and test it, only to find out that something doesn't work. By contrast, if you write bits at a time, you will know right away what code you wrote caused things to be wrong.
3. Apply the scientific method: have in mind hypothesis for what might be wrong, design a quick experiment to test your hypothesis, draw conclusions, and repeat.
4. Fail quickly. If you're working on a larger scale program that processes a lot of data, do not wait for several minutes for data to load every time you make a small change. Instead, come up with the minimum, simplest experiment you possibly can which will tell you whether your code is correct or not.
5. Don't forget that you can write code to help you automate debugging. Otherwise, it's sometimes tedious to repeat the same steps over and over again as you're changing things.
6. Know when to walk away. We often get stuck in loops wanting to resolve things, but then our logical thinking goes out the window and we start randomly trying different things. Even if you're up against the clock, it is often good to take a little break and come back again a little bit later.

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Schedule

Outlined below is the schedule for the course, including lecture topics and assignment due dates. All assignments are due at 11:59PM on the date specified. The specific dates of different topics are subject to change based on the pace at which we go through the course.

Lecture	Date	Lectures (click for notes)	Readings/Links	Assignments/Deliverables
Unit 1: Intro To AI / Classical Search And Planning
1	Mon 8/28/2023	What is AI? Python Basics: Variables, Lists, Conditionals, Methods, Loops	Slices from today Python reference: Sheehy Ch. 2 learnpython.org Bender/Hanna: "AI Causes Real Harm. Let's Focus on That over the End-of-Humanity Hype" Arvind Narayanan slides on AI Snake Oil Kapoor/Narayanan: How To Prepare for The Deluge of Generative AI on Social Media beepytunes.com	Assignment 0: Python Self Study Module Out
	Tue 8/29/2023			Assignment 1: Welcome To CS 477 Out
2	Wed 8/30/2023	Reinforcement Learning, One Armed Bandit Problem Python Objects, Object References	Sheehy Ch. 3 The bandit game Class notes on multi-armed bandits and reinforcement learning Sutton/Barto Ch. 2	Assignment 0 Parts 1-3 Due before Class
3	Fri 9/1/2023	Python Dictionaries, Finite State Machines	Class Exercise: Choose Your Own Adventure w3schools Dictionary Reference Geeks for Geeks Dictionary Reference Class Notes on Loops / Dictionaries	Assignment 0 Parts 4,5 Due before Class
4	Mon 9/4/2023	Tree Search, Graph Search: Breadth-First Search (BFS), Depth-First Search (DFS)	Class Exercise: Solving A Maze Blind Search Class Notes	Assignment 1 Due Assignment 2: The Rush Hour Problem Out
5	Wed 9/6/2023	Iterative Deepending and DFS Path Search, Backtracing Solutions, Abstract Search Spaces	Python code for maze search
6	Fri 9/8/2023	8-Puzzle, Uniform Cost Search	Class Exercise: 8-Puzzle
7	Mon 9/11/2023	Greedy Best-First Search, A* Search	Notes on Uniform Cost, Greedy Best-First, and A* Search Common Misconceptions About A* Search (Optional)	Assignment 2 Part 1 Due
8	Wed 9/13/2023	Consistent Heuristics, Begin Game Trees
9	Fri 9/15/2023	Ethics #1: Bias, Social Media, Current vs Future Harms		Heuristics mini exercise Due
10	Mon 9/18/2023	Game Trees: Alpha-Beta Pruning, Expectimax		Assignment 2 Part 2 Due
Unit 2: Probabalistic Knowledge Representation
11	Wed 9/20/2023	Markov Chains, Basics of Probability and Conditional Probability	Berkeley CS 188: Crash Course on Probability for AI Class Exercise: Markov Chains of Characters Class notes on markov chains for text documents	Probability Module Due Before Class Assignment 3: Markov Chains for Text Processing Out
12	Fri 9/22/2023	Bayes Rule, Begin Naive Bayes Classification	Three Blue One Brown Video on Bayes Rule Class Notes on Bayes Rule And Bayesian Classifiers Think Bayes Notes	Bayes Rule Module due before class
13	Mon 9/25/2023	Naive Bayes Classification And Bag of Words	Naive Bayes Class Notes Class Exercise: 2016 Presidental Debates Bag of Words Naive bayes for detecting computer viruses
14	Wed 9/27/2023	Hidden Markov Models	Class Exercise: Captain H. Markov Class notes on Hidden Markov Models / The Viterbi Algorithm Jurafsky/Martin Notes on HMMs A Statistical Approach To Machine Translation	Bayesian Classification Module due before class
15	Fri 9/29/2023	Bayes Filtering, Robot Localization	Kalman filter article Particle filtering article
16	Mon 10/2/2023	The Viterbi Algorithm	Class Exercise: Robot Localization	Assignment 3 Due
17	Wed 10/4/2023	Ethics #2: Corporate Capture And Colonial Practices
18	Fri 10/6/2023	Reinforcement Learning: Markov Decision Processes, Policy Iteration	Class Exercise: Pong AI Sutton/Barto Ch. 3 Tim Miller notes on policy iteration White 1985: Applications of MDPs	HMM Module due before class
Unit 3: Data Vectorization
19	Mon 10/9/2023	Data Vectorization, K-Nearest Neighbor Classification	Class Exercise: Digit Classification, K-Nearest Neighbor Notes	Euclidean Vectors / Data Vectorization Module Due Before Class Assignment 4: Fundamental Frequency Tracking And Pitch-Based Audio Effects Out
20	Wed 10/11/2023	Unsupervised Learning: KMeans Clustering	Class Notes on KMeans Clustering And Visual Bag of Words	Dot Products / Projection Module Due Before Class
21	Fri 10/13/2023	Unsupervised Learning: Projected Variance, Principal Component Analysis	Class Exercise: Eigen Digits PCA Explained Visually Loop Ditty Original Paper on Eigenfaces	Matrix Module Due Before Class
--	Mon 10/16/2023	Fall Break		No CS 477 Class. Enjoy the break!
22	Wed 10/18/2023	Logistic Regression, Introduction To pytorch and data loaders	PyTorch Datasets & DataLoaders Documentation Andrew Ng's Notes on Logistic Regression	Neural Networks Module 1 Exercises 1-3 Due Before Class
23	Fri 10/20/2023	Logistic Regression And Gradient Descent	Logistic Regression Class Notes	Rest of Neural Networks Module Due Before Class
24	Mon 10/23/2023	Softmax, Multi-Class Logistic Regression	Class Exercise: Logistic Regression on MNIST	Assignment 4 Due Assignment 5: Logistic Regression on Movie Reviews Out
25	Wed 10/25/2023	Finish Multi-Class Logistic Regression
26	Fri 10/27/2023	Multi-Class Logistic Regression in Torch, Mini-Batch Gradient Descent	Topology of neural networks Tensorflow.js Playground	Softmax Module Due Before Class
Unit 4: Neural Networks And Deep Learning
27	Mon 10/30/2023	Intro To Feedforward Neural Networks, Multilayer Perceptrons	Interactive Disc Separating Demo	Multi-Class Logistic Regression And Feedforward Neural Nets Module Due Before Class
28	Wed 11/1/2023	Backpropagation	Class Notes on Backpropagation through Multilayer Perceptrons (MLPs) "Yes, You Should Understand Backprop" -Andrej Karpathy Andrej Karpathy's Video on Micrograd (A fully fledged, general backprop implementation from scratch) Stanford CS 231N Backpropagation in Code
29	Fri 11/3/2023	Convolutional Neural Networks	Live 2D convolution demo Stanford CS 231n Notes on Convolutional Neural Networks Convolution Arithmetic Animations	Assignment 5 Due
30	Mon 11/6/2023	Cats vs Dogs, Overfitting Strategies: Dropout, Data Augmentation	Class Exercise: Cats vs Dogs with Data Augmentation Pytorch data augmentation for images Original Paper on Dropout	Backpropagation Module Due Assignment 6: Build Your Own Multilayer Perceptron (BYOMLP) Out
31	Wed 11/8/2023	Unsupervised Learning with Autoencoders	Class Exercise: Digits Autoencoder
32	Fri 11/10/2023	Autoencoders continued
33	Mon 11/13/2023	Adversarial Attacks on Images
34	Wed 11/15/2023	Ethics #3: Intellectual property and AI / Music in AI	Guest visit from Ben Cantil, product lead at DataMind Audio
35	Fri 11/17/2023	Variational Bayes, Variational Autoencoders	Jeremy Jordan Notes on Variational Autoencoders Evan Jang Notes on Variational Bayes Irhuam Shafkat: Intuitively Understanding Variational Autoencoders
36	Mon 11/20/2023	Work day		Assignment 6 Due
	Tue 11/21/2023			Last day to drop courses with a "W"
--	Wed 11/22/2023	Thanksgiving		No CS 477 Class. Enjoy the break!
--	Fri 11/24/2023	Thanksgiving		No CS 477 Class. Enjoy the break!
37	Mon 11/27/2023	Diffusion Models for Generative AI	AI Summer Notes on Diffusion Models Lilian Weng Mathematics Behind Reverse Diffusion Extracting the training data from diffusion models	VAEGAN Module: VAE Part Due
38	Wed 11/29/2023	Generative Adversarial Networks (GANs)	PathMind: A Beginner's Guide To Generative Adversarial Networks neptune.ai notes on GANs and failure modes
	Thu 11/30/2023			Assignment 7 Part 1: This Cat Doesn't Exist Out
Unit 5: Deep Learning Through Time
39	Fri 12/1/2023	Recurrent Neural Networks	Andrej Karpathy: The Unreasonable Effectiveness of Recurrent Neural Networks Illustrated Guide to LSTM's and GRU's: A step by step explanation	Ethics #4 Climate Questions / Discussion Due Final Ethics Paper Topic Proposal Due
40	Mon 12/4/2023	Attention	Attention Is All You Need Lillian Weng: Attention? Attention!
41	Wed 12/6/2023	The Transformer Architecture	Jay Alammar: Illustrated Transformer
42	Fri 12/8/2023	Ethics #5: Stochastic Parrots	On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?🦜 Stochastic Parrots Day retrospective Why google says we can melt eggs	Assignment 7 Part 1 Due
	Fri 12/15/2023			Assignment 7 Due Final Paper Due
	Sun 12/17/2023			Final Paper Peer Feedback Due

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Grading

Breakdown

Programming Assignments	72%
Class Engagement, Pre-Class Prep, Ethics Reading	17%
Final Ethics Paper	11%

Deadlines Policy

I have been very flexible in the past, but I have observed that this is usually to the detriment of students who let certain assignments drag on well past the deadlines and fail to move onto subsequent assignments, ultimately failing the course or getting artificially low grades that do not reflect their abilities. Moving forward, I would like to prevent this, and I would like all students to learn how to triage and submit "good enough" work on individul assignments so they can keep moving, and to practice valuable accountability skills that will benefit them in future endeavors past Ursinus. At the same time, I recognize that many things happen outside of the classroom that are beyond your control; life is inherently difficult and unpredictable, particularly for those who come from different backgrounds and who have responsiblities beyond the classroom. Therefore, I am building an oxymoronic "rigid flexibility" structure into the course, without exceptions outside of official accommodations. The rules are as follows:

• All programming assignments are due at 11:59PM EST on the date(s) stated on the schedule

• You have 5 free late days to use on programming assignments throughout the semester. These will go down to the decimal point if you submit minutes or hours after a deadline, so students don't have to fret about submitting at, e.g., 12:05 AM. You will get extra credit for any days you do not use, but no work will be accepted once the lateness days are used up

• I will drop the lowest programming assignment score at the end of the semester to help you adjust to this new policy.

• Pre-class content modules that are submitted after class will get half credit.

• Ethics discussions questions that are not submitted on time will get 0 credit.

To reiterate, you will find lots of flexiblity built into these policies, but they are designed to prevent you from getting too stuck on an individual assignment, and also to incentivize people to come prepared for lively class discussions. I hope we will all have more positive experiences moving forward with this.

Letter Grades

Letter grades will be assigned on the scale below at the end of the course.

A+: 99+ A: 93-98.9 A-: 89.5-92.9

B+: 87-89.49 B: 83-86.9 B-: 79.5-82.9

C+: 77-79.49 C: 73-76.9 C-: 69.5-72.9

D+: 67-69.49 D: 63-66.9 D-: 59.5-62.9

F: 59.49 and below

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Classroom Environment

Inclusive Environment

Computer science is a field that has historically been and continues to be steeped in inequalities. As we will see, modern AI and machine learning techniques have added fuel to the fire, so there is a special role and responsibility that those who have AI/ML knowledge have to play in being good stewards around both the technology and within work environments.

Within class interactions, my goal is to foster a environment in which students across all axes of diversity feel welcome and valued, both by me and by their peers. Axes of diversity include, but are not limited to, age, background, beliefs, race, ethnicity, gender/gender identity/gender expression (please feel free to tell me in person or over e-mail which pronouns I should use), national origin, religious affiliation, and sexual orientation. Discrimination of any form will not be tolerated.

A slightly more subtle thing that we want to watch out for in class is not to assume anything about others based on implicit biases. I have heard from quieter students at Ursinus from different backgrounds in the past that they sometimes feel their contributions aren't valued from their group members and that they're talked over during collaborations. So please be mindful of this and be sure to curb this behavior in yourself if you notice it...this is something immediate you can to do help stem the leaky pipeline in our field.

.Furthermore, I want all students to feel comfortable expressing their opinions or confusion at any point in the course, as long as they do so respectfully. As I will stress over and over, being confused is an important part of the process of learning computer science. Learning computer science and struggling to grow is not always comfortable, but I want it to feel safe. In other words, I will regularly keep you at the boundary of your comfort zone with challenging, real-world assignments, but I want you to feel comfortable with me and your peers and respected as a learner during the process.

Finally, I am aware that, particularly during the pandemic, there are a variety of factors that may make it difficult to perform at your best level in class. At Ursinus, we are fortunate to have quite a mix of students from different backgrounds, many of whom need to work part time, and an increasing number of whom are commuters and have family obligations. If you find yourself having difficulty performing at the level that you want and/or turning assignments in on time because of any of these issues, please communicate with me, and we can come up with a solution together (I will gently reach out if I notice any slips even if you don't communicate). This is an exciting capstone course, and I want to work to keep your excitement alive, regardless of your personal circumstances. We will get you through the course as long as the lines of communication are open and we work together. You belong in CS!

Evening Pfahler 106 Work Jams 🍇🍓🫐🫙🎵 🎵 🎵

To foster work and collaboration, I've booked Pfahler 106 from 7PM - 10PM during every week night, Monday - Friday, during the semester for people to go and work on assignments together. I've witnessed amazing collaborations happen in my office hours the past two years holding them in classrooms, and I want to encourage that kind of community to continue more outside of my own office hours. To that end, each student will be required to attend at least 3 evening sessions. I'll have snacks stored in that room, so your job will simply be to show up, take out the snacks, and stay for at least two hours and work. You can sign up for any evening throughout the semester, but you must sign up for at least one evening before midterms. I will be having the same requirement in my CS 271 class this fall, so there should be some great cross-pollination.

When you attend one of these, submit an entry on one of the assignments on canvas indicating the date you went. Also, if you're comfortable, take a selfie with other people there and post it on the Maker club discord!

Note on Commuter Students

If you are a commuter student, you are welcome to earn your 3 points by going to 3 in-person sessions. However, I understand this might be difficult logistically. So commuter students only have to attend one in person. For the other two points, commuter students have the option to provide answers to 2 questions on discord for each point, for 4 question answers total.

Ethics Discussion Expectations

We will be wading into some difficult issues in our ethics discussions, and race will be explicitly centered in many of them. Things will probably be fine even if we don't have "the talk," but I want to make absolutely certain we're on the same page. In particular, there are three important ground rules we will all need to abide by (myself included)

1. Everyone in the class is a unique individual with a unique set of life experiences and views. No person should be made to feel that they are the "representative" or "token" for any societal group, particularly since we are so demographically imbalanced in CS. People should only speak up if they feel comfortable.
2. It's important that we all go into this with a good faith attitude. We live in a fractured nation, so let's use this class as a petri dish for developing what productive discussions around contentious social issues might look like. I know for a fact that there are people in this class on opposite sides of the political spectrum from each other, but if we can all agree that we're working on this together and trying to figure it out, we will be OK. Right now, we're just Ursinus people trying to talk through difficult issues.
3. Someone will inevitably offend someone else. This happens in the course of human events. In the spirit of #2, what we should do in this instance is to establish a careful protocol to move forward in a productive way. What I'd like to propose is that if someone feels offended by something someone else says, and they are willing to call it out, the offended party immediately says "burn." The person who said something that was perceived as offensive should immediately respond with "aloe." This quick intervention reminds us that we're all actually on the same team, and we want to learn more about where each other is coming from. At that point, the burned person can explain why they feel that way, and the alloe person can listen and clarify in a respectful manner. Often, people have no idea what they said might be offensive, so it's a good time to talk through things carefully and to learn without shaming or ostracizing anyone

Classroom Attendance And Etiquette

Students are expected to attend class in person. We're shooting for engagement over mere attendance; students are expected to be active in class exercises and to be fully invested in the class (i.e. no internet browsing). Students who are unable to attend class for significant reasons (whether isolation or quarantine for students who have received a positive test, those experiencing Covid-related symptoms while awaiting test results, or other issues that make it difficult to attend class) should work pro-actively to make up any class exercises that they missed. To help with this, I will do my best to put up Youtube videos from other instructors on topics that we cover.

Finally, students are expected to follow any college policy requiring mask wearing on campus, in addition to following any guidance faculty provide for their individual classes.Masks should be available in every academic building, if needed.

Maximizing Your Communal Experience

Here are ways students can maximize their experience as a class community, and which could lead to extra credit in certain situations.

• Helping to teach a student a topic during student office hours.
• Certain calls for participation in class
• Particularly helpful or insightful messages on Discord
• Finding mistakes in class notes or on the assigned homework

Discord Communication Policy

Since this is a class-wide communication, the following rules apply to online communication

• Students are expected to be respectful and mindful of the classroom environment and inclusivity standards. They are equally applicable to a virtual environment as they are in class.
• Students are not permitted to publicly share direct answers or questions which might completely give away answers to any homework problems. When in doubt, please send me a direct message.
• I will attempt to answer questions real time during student office hours. Otherwise, I will make every attempt to respond within 24 hours on weekdays. I cannot be expected to respond at all on Saturdays or Sundays or outside of 10AM-8PM on weekdays, so please plan accordingly. (Of course, students can and should still respond to each other outside of these intervals, when appropriate).

The points above are part of a more general term referred to as "netiquette." Please refer to the chart below, provided by Touro College

Collaboration Policy

Overall Philosophy

The collaboration policy for this class walks the line between encouraging openness and collaboration during a challenging learning process, while also making sure that each students is progressing technically at an individual level without relying on 100% on other classmates. Communication between students is allowed (and encouraged!) on most assignments, but it is expected that every student's code or writeups will be completely distinct. General rules are as follows:

• Do not copy code off of the Internet

• Use of ChatGPT or other large language models is not permitted in this class. Later in your career once you've mastered the fundamentals we're covering, it's more appropriate to use it as a co-pilot. But you have to learn how to do it on your own first. Furthermore, since these models are optimized to sound convincing rather than to output correct information, it's quite likely that they will introduce bugs into code that they generate. So it's likely it will be easier to write your code from scratch anyway.

  Finally, on a personal note, I have a lot of responsibilities to juggle in my job. Please don't waste my time by sending me code that's not yours. As those who have had me before know, I put a lot of time into feedback to help you grow. Take advatage of that.

• Please do cite any sources in addition to materials linked from the course website that you used to help in crafting your code and completing the assignment

Assignment Buddies

To encourage collaboration, students will be allowed (not required) to choose one or more "buddies" to work "near" during the programming assignments. Students are still expected to submit their own solutions, but they are allowed to provide substantial help to each other, and even to look at each others' code during the process. Students should indicate their buddies in the README upon assignment submission. Please let me know if you would like a buddy but are having trouble finding one.

Collaboration Scenarios Table

Below is a table spelling out in more detail when and how you are allowed to share code with people (table style cribbed from Princeton CS 126).

* You may view and copy code from class exercises and class resources without citing them, but you should not copy solutions from previous semesters that the instructor may have provided

NOTE: The terms "exposing" and "viewing" exclude sending or ingesting electronically, which would be considered copying. Exposing and viewing are normally done in the context of in-person working or in the help room. When students work remotely, what this means is that buddies can screen share as they are working through things, but they should not send code directly.

NOTE ALSO: "Other people" includes internet sources.

If the collaboration policy has been violated in any way, regardless of intent, then it may be an academic dishonesty case, and it will be referred to the Associate Dean for Academic Affairs. I am required to make this report in every occurrence, so it is best to speak with me first if there are any questions about the policy or expectations. You should feel free to have these conversations with me anytime prior to making your submission without fear of penalty.

On a more personal note, though a willful violation of academic honesty may seem merely transactional to a student, faculty take violations very personally, as they are disrespectful to the time and effort we put into our courses. I would also like to emphasize that your reputation is much more important than your grades. The recommendations we as faculty write go a long way, and we are much happier to write positive recommendations for students with lower grades who show grit and growth than we are to write recommendations for students with higher grades who have cheated.

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Other Resources / Policies

Accommodations

In addition to our general awareness diversity, Ursinus College is also committed to providing reasonable accommodations to students with disabilities. Students with a disability should contact the Directory of Disability Services ASAP. Dee Singley is located in the Center for Academic Support in the lower level of Myrin Library. Please visit this link for more information on the process. I will do my best to accommodate your requests, and they will be kept completely confidential.

One on One Tutoring

One on one tutoring for up to two hours per week is available through the institute for student success. Please click here to fill out a Qualtrics survey if you'd like to take advantage of this.

Let's Talk

Mental health care is increasingly recognized as a crucial service for the undergraduate population. Please visit this link for more information about complementary counseling services provided by the college. The Wellness Center has a virtual drop-in crisis hour at 2-3 pm each weekday, which is available for students in crisis who need to be seen immediately by a clinician. If you are still hesitant to go, take me (Professor Tralie) as an example of someone who has benefited greatly from talk therapy and medication in the past. I am happy to discuss this in student office hours in more detail.

Title IX

Title IX is a federal law, under which it is prohibited to discriminate on the basis of gender. The Title IX Coordinator is available to receive inquiries and to investigate allegations in this regard.

Inclement Weather Policy (aka COVID Policy)

In the event that the College closes due to inclement weather or other circumstances (such as a COVID lockdown), our in-person class sessions, drop-in student office hours, or other meetings will not be held. I will contact you regarding our plan with regard to rescheduling the class or the material, any assignments that are outstanding, and how we can move forward with the material (for example, any readings or remote discussions that we can apply). If necessary, I may schedule online virtual sessions in lieu of class sessions, and will contact you with information about how to participate in those. I will communicate this plan to the department so that it can be posted on my office door if it is feasible to do so. This policy and procedure will also apply in the event that the College remains open but travel conditions are hazardous or not otherwise conducive to holding class as normal. Should another exigent circumstance arise (for example, illness), I will follow this policy and procedure as well.