Course Information

Introduction

This course provides a broad introduction to the foundations of modern artificial intelligence (AI) and will also develop advanced programming and data processing skills essential for upper-level courses in computer science. Topics will include advanced Python programming, mathematical foundations, data processing, machine learning methodology, neural networks, and large language models. Throughout the course, students will examine the ethical issues that arise in AI and its societal impacts. The course culminates in a final project. Students will gain core skills that will prepare them for a deeper examination of these issues in both later courses and independent projects.

The course is suitable for any student interested in gaining a deeper understanding of AI regardless of their intended major.

Prerequisite: Completion of CPSC 21 or its equivalent.

Goals for student learning

• Gain a deeper understanding of modern AI systems including neural networks and generative models for both text and images.
• Work with data from acquisition to application, including understanding the limitations and potential pitfalls at each stage of the data pipeline.
• Learn about the societal implications of technology and different interdisciplinary tools for analyzing these systems.
• Develop more advanced programming skills in Python, including the use of notebooks, libraries, APIs, and other object-oriented programming techniques.
• Provide mathematical foundations for AI and ML including linear algebra and basic statistics.
• Prepare students for upper-level applications courses.
• Learn to work effectively as a team on an open-ended project.

Grading

Reading

Rather than using a single textbook, we will be using materials from a variety of sources. All of the materials will be available online.

Our class meetings will be a combination of lecture and discussion. To be ready to participate in the discussion will require some preparation on your part. We recommend that you complete the week's reading prior to the first class meeting that week. We expect that you will typically need to spend only about 30 minutes per week on the reading.

Policies

Labs

Labs will be available on Thursdays at noon, and will be due by the following Wednesday before midnight. Even if you do not fully complete a lab, you should submit what you have done to receive partial credit.

You will work solo on the first few labs, and then with a partner for the remainder of the labs.

You have two late days that you may use on any lab, for any reason. If you are using a late day, you must contact your lab instructor by email or private message on EdStem to let them know.

Your late days will be counted at the granularity of full days and will be tracked on a per-student (NOT per-partnership) basis. That is, if you turn in an assignment five minutes after the deadline, it counts as using one day. For partnered labs, using a late day counts towards the late days for each partner. In the rare cases in which only one partner has unused late days, that partner's late days may be used, barring a consistent pattern of abuse.

If you feel that you need an extension on an assignment or that you are unable to attend class for two or more meetings due to a medical condition or other extenuating circumstance, please let your instructor know as soon as possible.

Academic Integrity

Academic honesty is required in all of your work. Under no circumstances may you hand in work done with (or by) someone else under your own name. Your code should never be shared with anyone; you may not examine or use code belonging to someone else, nor may you let anyone else look at or make a copy of your code. The only exception to this policy, is that you may freely share code with your lab partner. Also, you may not share solutions after the due date of the assignment.

Failure to abide by these rules constitutes academic dishonesty and will lead to a hearing of the College Judiciary Committee. According to the Faculty Handbook: "Because plagiarism is considered to be so serious a transgression, it is the opinion of the faculty that for the first offense, failure in the course and, as appropriate, suspension for a semester or deprivation of the degree in that year is suitable; for a second offense, the penalty should normally be expulsion."

Discussing ideas and approaches to problems with others on a general level is fine (in fact, we encourage you to discuss general strategies with each other), but you should never read any other student's code or let another student read your code. All code you submit must be your own with the following permissible exceptions: code distributed in class and code given in the readings. Regardless of the source, you should always include comments that indicate on which parts of the assignment you received help, and what your sources were. You may not share your solutions even after the due date of the assignment.

Any code, text, or content not created exclusively by you and used without attribution is plagiarism. This is true regardless of whether the original source was a scholarly text, another student, an online platform (e.g. StackOverflow), or a generative model (e.g. ChatGPT). Using resources such as these may be appropriate under some circumstances, and not under others, but regardless you must always properly acknowledge and cite the source of the information. When in doubt, add a statement of attribution! In addition, when using any type of generative AI, you must also describe how it was used, e.g. by giving the prompt.

The use of generative AI tools (e.g. chatGPT, GitHub CoPilot, etc.) without permission is also considered to be unauthorized collaboration with an outside source and is a violation of our academic integrity policy. If you feel these tools would be appropriate in a given context, feel free to ask the instructor. Note that even if permission is granted, these sources must be properly attributed.

Here are some examples of what this might look like:

Academic Accommodations

If you believe you need accommodations for a disability or a chronic medical condition, please visit the Student Disability Services website for details about the accommodations process. Since accommodations require early planning and are not retroactive, contact Student Disability Services as soon as possible. You are also welcome to contact the instructional staff privately to discuss your academic needs. However, all disability-related accommodations must be arranged, in advance, through Student Disability Services.

Support

Feel free to attend any office hours that are convenient for you.

Ninja support

The CS Ninjas will assist us in our lab sessions and will also run study sessions in the evenings. Students are also strongly encouraged to attend the evening study sessions for extra help in completing labs.

Ninjas are instructed to help guide you through any topics, but will not provide solutions/direct answers to programming problems. If you are having troubles with your programming assignment, please be prepared to describe what steps you have already taken to solve the problem. (The same goes for seeking help during office hours and/or on ed). While our ninjas are dedicated, please do not ask for their help outside of lab and ninja sessions--they are students just like you who need some off time to finish their own course work!

Ed Stem

To provide additional help we have established a class discussion forum through Ed Stem. If you have questions outside of ninja sessions and office hours, you can post the question to the class and/or privately to the instructors through ed. This allows students to see common problems and to also engage in discussions about course topics. Students are expected to regularly check the discussion page and use this in place of emailing the instructors directly about course content and labs.

Schedule - M/W

Schedule - T/Th