Interested in a verified certificate, a professional certificate, or transfer credit and accreditation ?
This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, machine learning, large language models, and other topics in artificial intelligence as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.
How to Take this Course
Even if you are not a student at Harvard, you are welcome to “take” this course for free via this OpenCourseWare by working your way through the course’s seven weeks of material. If you’d like to submit the course’s seven projects for feedback, be sure to create an edX account , if you haven’t already. Ask questions along the way via any of the course’s communities !
- If interested in a verified certificate from edX , enroll at cs50.edx.org/ai instead.
- If interested in a professional certificate from edX , enroll at cs50.edx.org/programs/ai instead.
- If interested in transfer credit and accreditation from Harvard Extension School , register at web.dce.harvard.edu/extension/csci/e/80 instead.
- If interested in transfer credit and accreditation from Harvard Summer School , register at web.dce.harvard.edu/summer/csci/s/80 instead.
How to Teach this Course
If you are a teacher, you are welcome to adopt or adapt these materials for your own course, per the license .
CS47100: Introduction to Artificial Intelligence (Spring 2023)
Course Information
Artificial intelligence (AI) is about building intelligent machines that can perceive and act rationally to achieve their goals. To prepare students for this endeavor, we cover the following topics in this course: Search, constraint satisfaction, logic, reasoning under uncertainty, machine learning, and planning. There will be four assignments in the form of both written and programming problems.
Pre-requisites:
- CS251 Data Structures (grade of C or better)
- [AIMA] S. Russell and P. Norvig (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th Edition. (ISBN:9780134610993)
- You can also use the 3rd edition and find the corresponding sections to read.
- Assignments: 40% (10% each)
- Midterm: 30%
- Final Exam: 30%
- Lecture slides and recordings will be posted on Brightspace.
- The instructor & TAs can be best reached through Ed Discussion. Please post your questions there instead of emailing TAs.
- During office hours or on Ed Discussion, please avoid posting partial homework solutions or asking TAs to "review" your code.
- Zoom links for office hours are posted on Ed Discussion.
- Tutorial for learning Latex with Overleaf: [Link]
Instructor & TAs
Raymond a. yeh.
Email: rayyeh [at] purdue.edu Office Hour: Mon 4-5PM Location: Zoom
Email: du286 [at] purdue.edu Office Hour: Thu 4-5PM Location: Zoom
Email: li4255 [at] purdue.edu Office Hour: Fri 10-11AM Location: Zoom
Email: li4178 [at] purdue.edu Office Hour: Wed 10-11AM Location: Zoom
Mir Imtiaz Mostafiz
Email: mmostafi [at] purdue.edu Office Hour: Fri 12PM-1PM Location: Zoom
Email: xinruw [at] purdue.edu Office Hour: Tue 10:30-11:30AM Location: Zoom
Ananya Singh
Email: singh745 [at] purdue.edu Office Hour: Mon 3-4PM Location: Zoom
Time & Location
- Time: Mon. & Wed. (5:30 pm - 6:45 pm)
- Location: Lilly Hall of Life Sciences G126
Other Resource
- BrightSpace
- Ed Discussion
Course Schedule
The following schedule is tentative and subject to change.
Late Policy
A 10% penalty will be applied (per day) to late assignments. Assignments that are more than two days late will not be accepted.
Academic Honesty
Please refer to Purdue's Student Guide for Academic Integrity . Academic dishonesty will result in an automatic zero on an assignment and your course grade will be reduced by one full letter grade. A second attempt will result in a failing grade for the course. It is one's responsibility to prevent others from copying your work.
Accessibility
Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, please contact the Disability Resource Center at: [email protected] or by phone at 765-494-1247 and the course instructor to arrange for accommodations.
Classroom Guidance Regarding Protect Purdue
Any student who has substantial reason to believe that another person is threatening the safety of others by not complying with Protect Purdue protocols is encouraged to report the behavior to and discuss the next steps with their instructor. Students also have the option of reporting the behavior to the Office of the Student Rights and Responsibilities . See also Purdue University Bill of Student Rights and the Violent Behavior Policy under University Resources in Brightspace.
University Policies
Please refer to additional university policies in BrightSpace .
CS 188 | Introduction to Artificial Intelligence
Spring 2021, lectures: mon/wed/fri 3:00–3:59 pm, online.
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.
We make lecture and Q&A recordings available as links to Google Drive, which you can find posted together with other materials on the Syllabus page of this website shortly after the lecture. These links will work only if you are signed into your UC Berkeley Google account. The recordings are also available on Kaltura , which is a service that UC Berkeley partners with that facilitates the cloud recordings of Zoom meetings. All recordings on Kaltura have automatically-generated captions available by default alongside some other useful controls, such as playback speed adjustment.
To access the channel with recordings for this course, please go to this website and create an account if you don’t have one already: https://kaltura.berkeley.edu
Once you have the account, you should be able to access and subscribe to videos in the channel by following this link .
- Lectures: Mon/Wed 1:30pm-2:50pm in NVIDIA Auditorium .
- Problem sessions: Fri 1:30-2:20pm in Thornton 102 .
- Office hours, homework parties: see the Calendar and the HW OH Queue .
- Try our new LLM powered bot on slack . Note: do not direct message any members of course staff on Slack.
- To contact all teaching staff, use Ed .
- For personal/sensitive matters, email [email protected] .
- Modules : All the course content has been broken up into short modules , which include slides, recorded videos, and notes.
- Lectures: Instructors go over the main modules more slowly and interactively. All lectures will be recorded and available on Canvas.
- Problem sessions: CAs work through practice homework and exam problems.
- Homework parties : CAs help students work through homework problems in small groups.
- Office Hours: Meet 1:1 with instructors and CAs. There are two types of CA office hours: homework OH (for help with homework questions) and general OH (to ask questions about course content from lecture).
- Looking at the writeup or code of another student.
- Showing your writeup or code to another student.
- Discussing homework problems in such detail that your solution (writeup or code) is almost identical to another student's solution.
- Uploading your writeup or code to a public repository (e.g., GitHub) so that it can be accessed by other students.
- Looking at solutions from previous years, either official or written up by another student, or found online.
Generative AI Policy: Each student is expected to submit their own solutions to the CS221 homeworks. You may use generative AI tools such as Co-Pilot and ChatGPT as you would use a human collaborator. This means that you may not directly ask generative AI tools for answers or copy solutions, and acknowledge generative AI tools as collaborators. The use of generative AI tools to substantially complete an assignment or exam (e.g. by directly copying) is prohibited and will result in honor code violations. We will be checking students' homework to enforce this policy.
Anyone violating the honor code policy will be referred to the Office of Judicial Affairs. If you think you violated the policy (it can happen, especially under time pressure!), please reach out to us; the consequences will be much less severe than if we approach you.
- Note that messages on public channels in slack are visible to other students and course staff.
- It is a strict violation of course policies to direct message course staff on slack, please keep interaction to public threads or reach out via Ed or the lead staff mailing list if you have questions.
- The student honor code still applies to messages and interactions on slack.
- (Required) Programming CS 106A , CS 106B
- (Required) Discrete math, mathematical rigor: CS 103
- (Required) Probability: CS 109
- (Required) Linear algebra: Math 51
- (recommended, but not required) Algorithms: CS 161
- (recommended, but not required) Systems: CS 107
- Russell and Norvig. Artificial Intelligence: A Modern Approach. A comprehensive reference for all the AI topics that we will cover.
- Koller and Friedman. Probabilistic Graphical Models. Covers factor graphs and Bayesian networks (this is the textbook for CS228 ).
- Sutton and Barto. Reinforcement Learning: An Introduction. Covers Markov decision processes and reinforcement learning (free online).
- Hastie, Tibshirani, and Friedman. The Elements of Statistical Learning. Covers machine learning from a rigorous statistical perspective (free online).
- Tsang. Foundations of Constraint Satisfaction. Covers constraint satisfaction problems (free online).
- Exam 1 (30%): Nov 2nd, 6-8 PM on Campus .
- Exam 2 (30%): Dec 13th, 3:30 - 5:30 PM on Campus .
- If you have a major time conflict for either exam, you should fill out this form by Friday, October 13 (week 3) .
Both exams will be in-person.
Exam Conflicts: Please reach out via the lead-staff mailing list if you have exam conflicts.
SCPD Students: SCPD students will need to nominate exam monitors for both exams and coordinate the exam process with the SCPD exams team . Please refer to this link for more information on the process. For any additional questions, please reach out to the SCPD exams team .
- Projects should be done in groups of 1-4 students.
- There are 5 milestones for the project throughout the quarter: interest form, proposal, progress report, video/poster, final report.
- Each project group will be assigned a CA mentor who will give feedback and answer questions.
- For inspiration, check out previous CS221 projects .
- See the project page for more details.
- --> Project interest form [p-interest] (due Tue Oct--> )--> Project proposal [p-proposal] (due Tue Oct--> )--> Project progress report [p-progress] (due Tue--> )--> Project final report and video [p-final] (due Tue--> )-->