Policies
Table of contents
- Description
- Enrollment
- Prerequisites
- Communication
- Projects
- Homeworks
- Accommodations and Extensions
- Lectures
- Discussions
- Office Hours
- Exams
- Grading
- Collaboration and Ethics
- Inclusion
- Support During Remote Learning
Description
This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.
By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
See the syllabus for slides, deadlines, and the lecture schedule. Readings refer to fourth edition of AIMA unless otherwise specified.
Enrollment
Course staff does not control enrollment; we have to follow the department’s Fall 2022 enrollment policies. We do not have any enrollment codes or any other way to let non-CS majors into the class. If you have any questions about enrollment, please reach out to the emails listed in the enrollment policies page.
Concurrent enrollment applications will be processed in the second week of classes. We will add all pending concurrent enrollment students to the course so you can follow along the first few weeks.
Prerequisites
- CS 61A or 61B: Prior computer programming experience is expected (see below)
- CS 70 or Math 55: Familiarity with basic concepts of propositional logic and probability are expected (see below)
CS61A AND CS61B AND CS70 is the recommended background.
The required math background in the second half of the course will be significantly greater than the first half. The self-diagnostic assignment Homework 0 will help check your preparation.
Course programming assignments will be in Python. We do not assume that students have previous experience with the language, but we do expect you to learn the basics very rapidly. Project 0 is designed to teach you the basics of Python and how the CS 188 submission autograder works. Project 1 is a good representation of the programming level that will be required for subsequent projects in this class.
Communication
The course schedule and all resources (e.g. lecture slides, discussion worksheets) will be posted on the course website: https://inst.eecs.berkeley.edu/~cs188/fa22/.
All course announcements and content/logistics questions will happen on Ed (the course discussion forum). We will be automatically enrolling everyone.
If you need to contact the course staff privately, please make a private question on Ed or email cs188@berkeley.edu.
Projects
There are 5 programming projects, submitted on Gradescope. We’ll enroll all students in Gradescope automatically.
Projects will by default be graded automatically for correctness, though we will review projects individually as necessary to ensure that they receive the credit they deserve. Projects can be submitted as often as you like before the deadline; we strongly encourage you to keep working until you get a full score.
Project 0 is to be completed alone. All other projects can be completed alone or in teams of two. If done in a team of two, the person who submits needs to tag the other team member on Gradescope. However, it is important that the submission reflects the understanding of both team members.
Projects lose 20% of their total point value per day turned in late, unless you have extensions or accommodations. However, you have 5 project slip days, which can reduce your late penalty for a project by 1 day each. (For example, if a project is due on August 1 11:59 PM, and you submit on August 2 12:30 AM, you will use one slip day.) We will automatically apply slip days to your active project submissions to maximize your total score. Slip days are consumed independently for each partner, but extensions will apply to both.
Homeworks
There are 10 homeworks, submitted on Gradescope. We’ll enroll all students in Gradescope automatically.
Homeworks cannot be turned in late unless you have extensions or accommodations. However, we will drop your lowest homework score.
Each homework has two components, described below:
Electronic HW component
The electronic portion will be automatically graded on Gradescope for correctness, and you can submit as many times as you like up to the deadline; we encourage you to work until you have fully solved this portion of the homework.
Written HW component
The written challenge question is meant to make you think beyond strict repetition of what is covered in class and is used to reinforce conceptual material that you will see on exams. For the challenge question only, you must show your work on paper or tablet and submit it to Gradescope. The challenge question will be graded on correctness, and you must show all necessary work to receive full credit. Solutions to the challenge question will be released after the homework is due.
We want you to focus on understanding the material in the homeworks, not necessarily maximizing your score. To that end, we will give full credit on written challenge questions that earn at least 80% of the points possible. Otherwise, the score will be divided by 0.8 (for example, if you get 70% on a written challenge question, your score for that assignment would be counted as 0.7/0.8 = 87.5%).
Homework is to be submitted individually, but may be discussed in groups. If discussed in a group, acknowledge your collaborators in the submission per standard academic practice.
Accommodations and Extensions
As instructors, our goal is to teach you the material in our course. The more accessible we can make it, the better. If you encounter any extenuating circumstances, please let us know as soon as possible so we can best help you succeed in the class.
If you ever need an extension during the semester, please fill out the extensions form.
The Disabled Students’ Program (DSP) supports disabled students at UC Berkeley. They offer a wide range of services and accommodations. If you are facing barriers in school due to a disability, apply to DSP! Students registered with DSP can expect to receive an onboarding email within a week of sending us your formal letter of accommodation through the AIM portal.
Lectures
We will have a 90-minute live lecture on Tuesdays and Thursdays, 5:00–6:30 PM. This is the listed lecture time on the course schedule.
We’ll try to livestream lectures over Zoom, but can’t promise that the tech will always work. The link to join lecture remotely is https://berkeley.zoom.us/j/93653672013.
Lectures will be recorded and recordings will be posted. Lecture attendance is not taken.
Discussions
TAs will hold weekly 1-hour or 1.5-hour live discussions throughout the week. Discussion sections start on August 31.
There are three types of discussion sections:
- Regular discussion sections focus on understanding the current material.
- Exam prep sections focus primarily on problem solving.
- Extended-time sections cover material at a slower pace and more in-depth, and are 1.5 hours long.
You can attend any discussion sections you want. (For example, you could attend one regular section and one exam prep section each week.)
Both in-person discussions and online discussions through Zoom will be available. Discussion attendance is optional, but we may offer 1-2% of extra credit for active discussion participation. We’ll try to post recordings of discussion worksheets, but no promises.
Office Hours
Office hours and HW/Project parties will be hybrid, so you can show up in-person or remotely. To request help, make a ticket on the office hours queue.
See the course calendar on the website for the office hours schedule and in-person locations. See Ed for the Zoom link for remote office hours. Office hours start on August 31.
Exams
The midterm is on Wednesday, October 12, 7-9pm PT.
The final exam is on Thursday, December 15, 11:30am-2:30pm PT.
Exams in CS 188 are challenging and serve as the main evaluation criteria for this class. More logistics for the exam will be released closer to the exam date.
If needed, we can offer remote exams at the listed time, or we can offer an alternate exam times immediately after the listed time. However, for exam security purposes, we cannot offer remote exams at any time except the listed time.
Grading
Overall grades will be determined from:
- Projects (25%)
- Homework Assignments (20%)
- Midterm (20%)
- Final Exam (35%)
| Grade | Overall Percentage |
|---|---|
| A | [85, 100] |
| A- | [80, 85) |
| B+ | [75, 80) |
| B | [70, 75) |
| B- | [65, 70) |
| C+ | [60, 65) |
| C | [55, 60) |
| C- | [50, 55) |
| D+ | [45, 50) |
| D | [40, 45) |
| D- | [35, 40) |
| F | [0 , 35) |
Staff may adjust grades upward based on class participation or extenuating circumstances. The grade of A+ will be awarded at staff discretion based on exceptional performance.
If you are taking the class P/NP, you will need to attain a letter grade of C- or higher AND take the final to pass. If you are a graduate student taking the class SUS, you will need to attain a letter grade of B- or higher AND take the final to pass.
A final grade estimator is available for informational purposes only. In the unlikely event that a bug results in a disagreement between the course policies and this tool, the grade calculated using the course policies will be the one that is submitted to the University. This tool is still a work in progress and many values are just placeholders. It will be updated closer to the end of the semester.
Collaboration and Ethics
Please note that obtaining, sharing, and posting solutions to homework or projects is a violation of academic integrity. This includes uploading project code, official solutions, your own solutions, etc. to any site that is accessible by other people, such as a public GitHub repository.
Homework/Project submissions should acknowledge all collaborators and sources consulted. All code and written responses should be original. We trust you all to submit your own work, but to protect the integrity of the course from anyone who doesn’t want to play by the rules, we will actively be checking for code plagiarism (both from current classmates and previous semesters), as well as written homework submissions that look eerily similar.
If you use a code snippet from a website like StackOverflow for a small task (for example, capitalizing a string), this is fine, but please cite your sources in your code with a comment.
Exams are expected to demonstrate your work, and your work alone. We have a zero-tolerance policy for any form of collaboration on exams. We are not lenient about cheating; in past semesters, CS 188 has caught upwards of 50 students for academic dishonesty and directly reported them to the Center for Student Conduct. An overwhelming majority (>90%) of the students were found guilty, and thus earned an “F” in the class and a mark on their transcript. Please, just don’t cheat. It’s not cool.
We sympathize with Kris Pister’s policy.
Inclusion
We believe in the crucial importance of creating a learning environment that is welcoming and respectful to students of all backgrounds. The following are specific steps that will help us in achieving this goal:
- If you feel your academic performance has been impacted negatively due to a lack of inclusion, or due to experiences outside of class such as current events or family matters, please reach out to the instructors and staff. Our job is not only to teach but to support you in every way we can.
- If something happens in the course that runs counter to the goal of making every student feel safe, respected, and welcome, please contact the head TA or the instructors; if you don’t feel comfortable contacting course staff, you can fill out this form to anonymously let the department know.
- You may also consult a departmental Faculty Equity Advisor, or fill out the anonymous feedback form for the College of Engineering for equity and inclusion related feedback.
- If you have a preferred name or set of pronouns that differ from your legal name, you may designate a preferred name for the classroom by following these steps.
- As a member of the CS 188 community, realize that you have an important duty to help other students feel respected in helping create an inclusive learning environment.
Support During Remote Learning
From the College of Engineering:
We understand that your specific situation may present challenges to class participation. Please contact the instructors if you would like to discuss these and co-develop strategies for engaging with the course.
The Student Technology Equity Program (STEP) is available to help you access a laptop, Wi-Fi hotspot, and other peripherals.
You will be alerted as to when synchronous sessions are about to be recorded. If you prefer not to be recorded, you may turn your video and microphone off. Please set your Zoom name to be the name you would like instructors to call you. You may optionally include your personal pronouns. Please set your Zoom picture to an appropriate profile picture of you to foster a sense of community and enhance interactions. If you are not comfortable using an image of yourself, you may use an appropriate picture of an avatar. We encourage participating with your video on to foster a sense of commnuity and enhance interactions. However, we understand that some students are not comfortable with video or may not be able to participate by video.