CS 410/1411 Course Missive Spring 2026

# CS 410/1411 Course Missive Spring 2026 [Foundations of AI and ML](https://browncsci410.github.io/ai-website-s26/) Serena Booth Time: M/W/F 1:00-1:50pm, Location: Salomon Center 001 Instructor Office Hours: Fridays 10:30-11:30am Location: CIT 427 ## Course Content This course will provide broad coverage of core topics in artificial intelligence (AI), as a prelude to students taking more in-depth AI courses later on. To this end, the course will introduce students to prevalent AI models, both logical and probabilistic, as well as algorithms to solve these models based on search, planning, reinforcement learning, and supervised and unsupervised machine learning. These ideas will be applied to develop basic natural language processing, computer vision, robotic, and multiagent systems, all with an eye towards building socially responsible AI. Note: CS 410 and CS 1411 share the same course staff, lectures, discussions, and assignments. Enrollment in CS 1411 is limited to graduate students. **Neither course can be capstoned.** ## Academic Learning Objectives By the end of this course, students will know: * Core AI models and algorithms, including logical and probabilistic frameworks, and how to apply them to natural language processing, computer vision, robotics, and multiagent systems tasks. By the end of this course, students will be able to: * Evaluate the effectiveness and appropriateness of competing AI models and algorithms, considering tradeoffs between their efficacy and efficiency. * Critically assess the societal impact of the deployment of AI systems, anticipating potential benefits and unintended negative consequences. ## Learning in the Age of Generative AI The goal of this course is to help students develop the skills required to work in field of AI. We aim to sharpen their critical thinking skills, which is arguably the most important ability humans can bring to the code development process in the rapidly evolving world of generative AI. As AI tools increasingly assist with coding tasks, the ability to think critically about problems and solutions, and the broader impact of AI systems, becomes paramount. We have designed the course to emphasize empirical experimentation with AI models and algorithms, which we deem as crucial for developing a deep understanding of the material and for engaging in thoughtful analyses. ## Prerequisites Programming experience and some degree of mathematical maturity are necessary for success in this course. The following are specific areas in which basic expertise is assumed, along with suggested courses for acquiring said expertise. * Comfort with programming: e.g., CS 200 or equivalent. * Comfort with probability and statistics: e.g., CS 220, APMA 1650, or APMA 1655 * Comfort with linear algebra*: MATH 520, MATH 540, or CS 530 *May be taken concurrently Overrides will not be given to students who have not taken the prerequisites. Prof. Booth will not reply to emails requesting such overrides. ## Course Schedule | Week | Topics | |------|---------| | 1 | Introduction to AI and Uninformed Search | | 2 | Informed Search | | 3 | Discrete Optimization | | 4 | Knowledge Representation | | 5 | Knowledge Representation Cont.| | 6 | Constrained Optimization and Mathematical Programming | | 7 | Introduction to Machine Learning and Linear Regression| | 8 | The Bias-Variance Tradeoff and Regularization | | 9 | Neural Networks and Deep Learning | | 10 | **SPRING BREAK** | | 11 | Reinforcement Learning | | 12 | Unsupervised Learning | | 13 | Anytime Algorithms and Introduction to Final Project | | 14 | Final Project Cont. | ## Assignment Schedule | Homework | Topic| Application | | --- | --- | ---| | 1 | Uninformed Search | Maze Solving| | 2 | Informed Search | Swapping Tile Game| | 3 | Adversarial Search |tic-tac-toe and Connect 4| | 4 | Boolean Satisfiability and Local Search | n-Queens and Sudoku| | 5 | Bayesian Networks | Diagnostics | | 6 | Constrained Optimization |Investment Portfolio Optimization and Energy System Planning| | 7 | Bias and Variance |Predicting Life Expectancy| | 8 | Neural Networks |Non-linear separators| | 9 | Reinforcement Learning | Blackjack | | 10 | Agentic AI | LLMs+PDDL | ## Textbook The primary textbook for this course is: [Artificial Intelligence: A Modern Approach](https://web.archive.org/web/20250125134421/https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf), Stuart Russell and Peter Norvig. This book provides comprehensive coverage of the core topics we will be exploring in this course. It is highly recommended that students refer to the relevant sections to supplement lecture notes, complete homework assignments, and deepen their understanding of the course material. *Since the textbook is available online, the materials cost of this course is $0.* ## Course Format ### Lectures CSCI 410 lectures are held on Mondays, Wednesdays, and Fridays from 1 PM to 1:50 PM in the Salomon Center 001. **You are highly encouraged to attend lectures.** Written notes and lecture capture will be posted on the website. ### Assignments Students will be assigned weekly homework assignments, released on (mostly) Wednesdays, and due (mostly) the following Monday at 11:59pm. Assignments will be available through the [course website](https://browncsci410.github.io/ai-website-s26/). To enhance the learning process, we recommend that students collaborate on homeworks, adhering to the limits of the collaboration policy described below. ### Discussions To complement weekly lectures, students are **required** to register for a weekly one hour discussion section, offering a collaborative environment where students can engage deeply with both core material and Socially Responsible Computing (SRC) topics. These discussions are intended to create an open environment for participation, promote critical thinking about the efficacy and efficiency trade-offs in AI models and algorithms, and encourage students to consider the broader societal implications of AI. Multiple discussion sections will be offered each week, with content alternating between technical and SRC concepts. These discussions are mandatory, and will form part of students' final grade (see grading breakdown below). #### Conceptual Discussions TAs will lead interactive conceptual discussions biweekly, based on the content in recently submitted homework assignments. These discussions are essential for reinforcing the course material and provide an opportunity to clarify any misunderstandings about the homework topics. A further goal of these discussion sections is to help students learn to reason about technical tradeoffs inherent in AI technologies. Discussion will thus focus on empirical investigations, specifically interpreting experiments that summarize the efficiency and efficacy of competing AI models and algorithms. To prepare for these discussions, students should: * Complete the relevant homework assignment, including the requisite READMEs that address the conceptual questions posed. * Review their submissions before attending their discussion section so that they are prepared to engage in productive discussions. #### Socially Responsible Computing Discussions TAs will also lead biweekly SRC discussions, which will focus on the ethical and societal implications of AI technologies. These sessions will encourage students to examine how AI systems can impact society. TAs will facilitate these discussions, guiding students through case studies, current events, and theoretical questions that challenge them to think critically about the role of AI in society. The goal is to foster a thoughtful, reflective approach to AI development, ensuring that students become not only technically proficient in AI but also socially conscious practitioners. #### Switching Discussion Sections You will be assigned a discussion section at the start of the semester based on your reported preferences. Please try your best to attend your assigned discussion section each week. If you absolutely must switch one week, you can email your upcoming discussion TAs *and* the TAs who lead the section you hope to switch into **at least 24 hours** before the scheduled discussion. If something comes up and you need to permanently change your discussion section, please email the HTAs. ### Final Project The course will culminate in a final project in which students program an agent to play the board game Go. Students will work individually to program their own AI agent that will compete with agents programmed by other students in the class and by the course staff. The final project consists of two parts: the first is due on **Wednesday, April 29th**, and the second on **Monday, May 4th**. The first part of the project guides students in creating strong Go-playing agents. In the final part, students will create their own agent by combining techniques from throughout the semester to build the best Go agent they can. Students will submit a final report about their agent, documenting their approach and their agents' performance in an experimental evaluation of their own design. They will be graded on the content of this report, the quality of their approach—including their creativity—and finally, to a lesser extent, their final agent's performance. ### Exams There will be a midterm on **Friday, March 13th**. ### Time Commitment All students should expect to spend 2.5 hours per week in lecture and 1 hour per week in discussion sections. In addition to these instructional hours, there will be 10 weekly homework assignments (4-8 hours), SRC readings (<1 hour), and optional textbook readings (1-2 hours). The final project is open-ended, but 40 hours over the course of 2 weeks, should suffice to produce acceptable work. In sum, students enrolled should plan to dedicate approximately 12 hours per week to this course, for a total of 180 hours over the course of a 15 week semester. ## Getting Help ### Staff Contact Information Professors: serena_booth@brown.edu HTAs: cs0410headtas@lists.brown.edu All TAs: cs0410tas@lists.brown.edu ### TAs Office Hours TA office hours will take place in a designated lab or conference room. You can find the timings and locations of the hours on the [hours page](https://browncsci410.github.io/ai-website-s26/#calendar) of the course website. Simply show up, start working, and raise any questions as they arise. TA office hours may be conducted in a collaborative group-style format or in the traditional 1-on-1 format. During 1-on-1 hours, TAs will use the [hours queue](https://hours.cs.brown.edu/login) to allocate their time to students. During group-style hours, TAs will circulate around the room, offering both conceptual and debugging help to multiple students simultaneously. If a question or issue is relevant to many students, the TA will switch to “teaching mode” to address the entire group. This group-style format is designed to maximize the time TAs can spend assisting students, and to encourage collaborative learning among students. TAs will choose the hours format to best accommodate student needs at the time. During a single hours session, TAs may switch back and forth between formats, or delegate different formats to different TAs if multiple TAs are present. ### EdStem In addition to in-person office hours, students may post their questions on EdStem, an online forum monitored by the TAs. **TAs will respond only to EdStem posts and not to emails**, except in a few rare circumstances described below. **Most questions related to assignments, logistics, or materials should be posted on Ed, not emailed.** Posts on Ed reach the entire course staff, allowing the next available TA to address your question. Please note that it may take up to 24 hours for you to receive a response. Note that the TAs will observe quiet hours from 12 am to 9 am. During these hours, you should not expect immediate responses, especially during high-demand times like before assignments are due or around exams. TAs will do their best to assist you, but please plan ahead. ### When to use EdStem vs Email By default, please **use EdStem** to contact the course staff. The only exceptions are listed below. #### When to Email a TA Directly 1. They initiated the communication. 2. They are your discussion leader and you are addressing a discussion issue or are switching sections for the week. #### Sensitive Issues For matters requiring privacy, such as extension requests, please email the HTAs or the professors directly. * **Extension requests:** Email the professor directly. * **Final Project or Logistical Issues:** Email the HTAs. Please do not email the course staff about regrades. Please proceed via Gradescope. ## Grading The grading breakdown is as follows: | Assignment | Count | Percentage | | -------- | ----- |-------- | | Homeworks | 10 |30% | | SRC Discussion Sections | 5 | 10% | | Conceptual Discussion Sections | 4 | 10% | | Midterm | 1 | 25% | | Final Project | 1 (2 parts) | 25% | **We leave ourselves flexibility to make minor adjustments to the relative weights of each homework.** ### Rubrics Grading rubrics for homeworks and the final project are developed by the professor in conjunction with the undergraduate TAs (UTAs) and available on all homework handouts. UTAs are responsible for grading all assignments, and any grading issues should be addressed via Gradescope regrade requests within one week of grade releases. If the issue has not been addressed after one week, please post a private question on EdStem. **Regrades:** Regrade requests can be requested on gradescope after assignment grades are published. The professor assigns all final grades and review individual assignment grades as necessary. Any major issues that warrant the professor involvement can be addressed to the professor directly via email. ### Discussion Section Grading Criteria Each discussion section will be graded out of 10 points. The breakdown will differ between Conceptual and SRC Discussion Sections. Note that each student's **lowest Discussion Grade will be dropped**. You can also think of this as getting 1 free discussion skip, in which case the 0 will be dropped. There are no discussion section makeups. #### Conceptual Discussion Section Conceptual Discussion Sections will be graded on a 10 point scale, where 5 points will be based on attendance and the other 5 points will be based on an exit ticket. Therefore, the base score a student can expect to earn is 5/10. The exit ticket will be scored based on accuracy. Conceptual Discussion Sections will also contain questions, and correctly answering them when called on earns a student +1, which may be awarded at most once per section. :::warning Note: The total score a student can earn will not go above 10. Penalties for inattentiveness (i.e. on phone, not engaging with peers when asked to, inappropriate conversing) will be applied at the TAs' discretion. ::: #### SRC Discussion Section SRC Discussion Sections will be graded on a 10 point scale, where 5 points will be based on participation and the other 5 points will be based on an exit ticket.  | Participation Points | Criterion | |--------|-----------------------------------------------------------------------------| | 5/5 | Actively participates with relevant contributions during the discussion. Participation is exceptionally high quality and consistent. Demonstrates critical thinking by offering thoughtful analyses or asking insightful questions. Shows respect for others’ viewpoints and engages in collaborative discussion. Comes prepared, having completed any required readings or assignments, and is ready to engage.| | 4/5 | Participates in discussion and activities. Demonstrates an understanding on content and points are relevant to the discussion. | | 3/5 | Attends section, but does not participate in discussion. | :::warning Note: 3/5 is not the lowest score, further penalties for inattentiveness (i.e. on phone, not engaging with peers when asked to, inappropriate conversing) will be applied at the TAs' discretion. ::: ## Guidelines for Discussions CS 410/1411 is a large class which can be intimidating to some students. As such, we offer the following guidelines intended to ensure that all interactions among students and between students and staff are conducted in a respectful and courteous manner. ### Guidelines for Discussion Sections * Be respectful: Make space/take space. Be conscious of how much space you take up or leave on the table during a discussion. Make space for others, but also know that there is equal space for you. * Be generous: Sometimes, it takes a few minutes to formulate a thought. If someone starts speaking while a thought is still half-baked, be patient—especially if they make a mistake early on. Be open to competing approaches to a problem. Encourage participation from everyone, especially those whose approaches differ from your own. * Be courteous: Even if you disagree with a speaker, listen intently and try not to interrupt. Others might have opinions or beliefs different from your own. Challenge or criticize an idea, not a person. * Be trusting: If someone says something that makes you uncomfortable, assume they didn’t intend to cause harm. Address the impact of their words, ideally in a private conversation rather than publicly, so they can learn from the situation. ## Policies ### Late Policy and Late Days Students will get 6 late days that will be applied chronologically to the homeworks. A max of 2 late days can be used per homework. Homeworks will generally be released by Wednesday afternoons and due the following Monday at 11:59pm, unless noted otherwise (e.g., because of a holiday). :::warning Additionally, note that late days cannot be applied to the final project. **Late final projects submissions will not be accepted.** ::: ### Extensions Extensions may be granted by the professors in extreme circumstances. If you are ill, please visit health services so you can provide a doctor’s note when requesting an extension. If you are under any other sort of duress, please seek advice from a dean. :::warning TAs cannot grant extensions. So students should contact the professors, not the TAs, to request extensions. ::: ### Flagging AI Submissions Homework submissions that show signs of unacceptable AI usage will recieve either a warning or a flag on Gradescope. Warnings will be given to homework submissions that are suspected of AI usage while flags will be given to homework submissions that show clear evidence of AI usage. Upon receiving a flag on an assignment, a student will get a 0 on that assignment and will be asked to come to Professor Booth's office hours for an oral exam. Upon receiving 2 flags, a student will get a 0 across all homework assignments and may be referred to the academic dishonesty committee. Upon receiving multiple warnings, we equally may lower grades, assign 0s to homeworks, or be referred to the academic dishonesty committee. ### Collaboration Policy Students are encouraged to discuss their work with their peers in CS 410/1411. They are also permitted to solicit help from generative AI—within limits. We encourage the use of learning modes if using LLMs. For instance, you can use "study and learn" mode in ChatGPT or "guided learning" mode in Gemini and ask it to quiz you on a topic of interest.  #### Working with Peers  It is useful when working on assignments to bounce ideas off other students, and we encourage you to do so. However, while collaborating with peers, you must follow these rules: 1. **Write only pseudocode together.** You can develop pseudocode together, but any Python code you submit must be written independently. 1. **Only discuss conceptual questions.** You can discuss how to approach conceptual questions with others (e.g., “Where should I start to develop a heuristic for Tic-Tac-Toe?”), but you must write your solutions on your own. You should not, for example, be discussing specific hyperparameter values. 1. **Submit only your own work.** Be sure that all code and written solutions you submit are written by you and only you. 1. **Do not share code or solutions.** Writing code or solving conceptual questions on a whiteboard or in any other medium that another student can see is forbidden. Take care to ensure that others do not copy your code or your solutions to conceptual questions. *Letting another student copy your work, even if you did the work entirely on your own, is a violation of the collaboration policy.* 1. **List the names of your collaborators in your README.** Include anyone you collaborated with, and what you worked on together. You may be asked at any time to explain your homework solutions in your Discussion Section or directly to the professor. *Failure to understand any part of the work you turn in is a violation of the collaboration policy.* :::warning **Note**: The rules for collaborating with peers apply to all assignments, including the final project. ::: #### Generative AI: ChatGPT, Copilot, etc. The use of generative AI tools in CS 410 is permitted within limits and encouraged when used in support of learning. We encourage the use of AI tools to reinforce course concepts. If you are using ChatGPT and Gemini for conceptual questions, use study or guided learning mode. Example Prompt: "You are an expert in AI and I am learning about PDDL in an AI course. Ask me questions about PDDL to verify that I understand the concept or find holes in my understanding." These types of educational discussions with LLMs are **acceptable and encouraged**. The use of ***autocomplete*** tools, such as GitHub Copilot or any other AI-assisted coding tools, **is strictly prohibited, unless specified in an assignment.** If a TA observes a student using such tools to complete coursework, it will be considered a violation of the collaboration policy. You can ask language models like ChatGPT to explain concepts (which they may or may not do correctly), but you cannot ask these tools to solve your homework problems for you. If a student chooses to engage a language model in a homework discussion, a TA-accessible link to the conversation history or a list of the prompts used must be included with the hand-in. If you cannot provide a transcript because the tool doesn't support that feature (i.e., autocomplete tools), then you shouldn't have been using that tool in the first place. As a rule of thumb, *you are allowed to ask a chatbot to answer questions whose solutions can be found in the textbook*. Examples include: - “Can you explain the key differences between supervised and unsupervised learning, and provide examples of when each is used?” - “Can you explain how the A* search algorithm works and why it is sometimes preferred to other search algorithms?” - If you want to understand how to represent a Markov Decision Process (MDP) in Python, you can ask: “Can you explain to me how to represent an MDP in Python?” - If you want to know how to implement a simple perceptron in Python, you can ask: “Can you write a basic perceptron model in Python and explain how it works?” You are *not* allowed to ask a chatbot to answer any questions on a homework assignment for you. Examples include: - Asking for assignment-specific code: “Please implement Q-learning for my grid world problem.” You are also not allowed to copy or paste any code generated by a chatbot into a homework or project submission. **Submitting code that was generated by a chatbot is considered plagiarism**, even if you retype that code yourself. If you have high-level design questions, such as, “How do I design an AI agent to solve a maze using blind search?” we recommend you ask a TA. Similarly, for debugging help, which is almost always homework specific, we recommend you ask a TA. If you ask a TA for help with code that it is clear you did not write or understand, you will be reported to the professor for potential academic dishonesty. #### Violations of the Collaboration Policy Any violation of this collaboration policy (e.g., unnatural similarities among students' submissions, suspicions of ChatGPT/Copilot) will be forwarded to the Dean of the College's office for review, to assess whether or not there has been a violation of Brown’s Academic Code. If you have any questions about this policy, please ask the course staff for clarification. *Not understanding our policy is not grounds for not abiding by it.* ## Diversity and Inclusion The computer science department is committed to diversity and inclusion, and strives to create a climate conducive to the success of students of women, students of color, students of any sexual orientation, and any other students who feel marginalized for any reason. If you feel you have been been mistreated by another student, or by a member of the course staff, consider reaching out to one of student advocates on the CS department’s Diversity and Inclusion Committee, Professor Booth, or Professor Tamassia (the CS department chair). We, the CS department, take all complaints seriously. ## Accommodations If you feel you have any disabilities that could affect your performance in the course, please contact SAS, and ask them to contact the course staff. We will support accommodations recommended by [SAS](https://studentaccessibility.brown.edu/). ## Harassment Please review Brown’s [Title IX and Gender Equity Policy](https://policy.brown.edu/policy/title-ix). If you feel you might be the victim of harassment (in this course or any other), you may seek help from any of the resources listed [here](https://campus-life.brown.edu/equity-compliance-reporting/title-ix).

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