Course Details

CS 5100: Foundations of Artificial Intelligence

Special thanks to Professor Raj Venkat to use the materials from his previous classes. This course has been developed to incorprate those materials, along with the latest advancements in artificial intelligence.

Schedule: Wed 6:00pm - 9:20pm

Location: Zoom + BK 310

Dates: Sep 3, 2024 - Dec 16, 2024

Instructor: Jin Yu | jin1.yu@northeastern.edu | Office Hours: Zoom (see “Staff” tab of Piazza Resources for schedule)

Piazza: Questions and lecture material are handled via Piazza | Sign up https://piazza.com/northeastern/spring2025/ds5010yu

Canvas: Course schedule and assignments are available via Canvas | Log in at https://northeastern.instructure.com/courses/226073

Teams: Office hours, Friday 11:30-12:30, are held virtually via Zoom | Please join Professor Jin Yu's Office Hours

Useful Textbooks:

Academic Integrity:

Be familiar with the university’s academic integrity policy on cheating and plagiarism.

Overview

This course provides a rigorous yet practical introduction to the foundations of artificial intelligence (AI). You'll explore how AI addresses complex real-world problems through principled mathematical frameworks and computational methods. Topics include search algorithms, machine learning, Markov decision processes, game playing, constraint satisfaction problems, probabilistic graphical models, and logic-based reasoning. Alongside theory, the course emphasizes hands-on experience with programming, data structures, and algorithms—building the skills essential for modern data science and intelligent systems.

Course Progression Requirement

This course is fast-paced and covers a lot of ground, so it is important that you have a solid foundation in a number of areas. Here are the basic skills that you need: Programming, Discrete math, Probability, Linear algebra, Algorithms, Systems.

Topics

  • AI history, ethics, responsibility
  • Machine learning, feature engineering, neural networks, back propagation
  • Search, dynamic programming, tree search, uniform cost search, A-star
  • Markov decision process, modeling, policy evaluation, value iteration
  • Reinforcement learning, Monte Carlo, Q-learning
  • Games, modeling, game evaluation, expectimax, minimax, expectminimax
  • Constraint satisfication problems, dynamic ordering, arc consistency, Beam search, local search
  • Markov networks, Gibbs sampling
  • Bayesian networks, probablistic programming, particle filtering, EM algorithm
  • Logic, propositional logic syntax, inference rules, first order logic
  • Conclusion

General Policies

Name and pronouns

Please let me know if you use a different name or pronouns from what appears the class roster. You may use a chosen name on Piazza and when submitting assignments and exams, but please be consistent and inform the instructors. The Northeastern LGBTQA Center can provide resources for changing your name and gender marker in the Northeastern system.

Please be kind and respectful to your fellow students regardless of identity or background. Students are expected to respect and use other students’ chosen names and pronouns. All students are expected to respect Northeastern’s commitment to diversity and inclusion.

Mental and physical health

Please reach out to me as early as possible if you have difficulty keeping up with class material or completing assignments for personal reasons. I am able to provide more accommodations and options for you earlier in the semester than later in the semester when deadlines are looming. The We Care program at Northeastern University is another resource available to you in times of stress.

Academic integrity

All students are expected to abide by the university’s academic integrity policy. Plagiarized work will not receive points in this course and may be reported. Authorized use of outside resources (including but not limited to third-party code) must be cited.

Title IX

Northeastern University strictly prohibits discrimination or harassment on the basis of race, color, religion, religious creed, genetic information, sex, gender identity, sexual orientation, age, national origin, ancestry, veteran, or disability status. Please review Northeastern’s Title IX policy, which protects individuals from sex or gender-based discrimination, including discrimination based on gender-identity. Faculty members are required to report all allegations of sex/gender-based discrimination to the Title IX coordinator.

Using Generative AI

Hoemworks: No AI use permitted. These assess your individual understanding and analysis. Any AI use will be treated as an academic integrity violation.

Programming and Debugging Help: You must be able to explain any AI-generated or online code to receive TA/instructor support.

Writing Assistance: If using AI to improve clarity, include an appendix with your original responses. Failure to do so violates academic integrity.

Note: this is for transparency only — no penalty for properly disclosed AI use. Ask if unsure about policy.

Remote Instruction

IMPORTANT: I will be teaching primarily onsite, with occasional remote sessions in case of snow or travel. Please refer to Canvas for Zoom links and check Piazza for the latest updates.

All course content is available online, and students may join via Zoom. However, some activities—like exams require live attendance during class hours (Boston time). Onsite participation is encouraged, and students are responsible for meeting any in-person enrollment requirements.

Please do not come to class in-person if you are experiencing symptoms of COVID-19 or other flu-like illness.

Technology

Piazza

Course administration, including all questions, course materials, and course announcements will be handled via Canvas.

Please use Piazza for your questions and queries instead. This allows us to track all course-related correspondence in a single location.

General questions that may be useful to other students should be posted publicly to the whole class. If your question is specific to you, or includes a partial solution, then post it privately to instructors only.

Please see this Stackoverflow guide for how to ask a good question.

Canvas

All assignments and exams will be posted on Canvas, and must be submitted on Canvas by the posted due date. Please do not email completed assignments or quizzes to instructors or TAs, or post them on Piazza.

Zoom

Classes will be broadcast synchronously via Zoom. Students can use Zoom to attend class virtually when in-person attendance is not possible.

Microsoft Teams

Virtual office hours will be held via Microsoft Teams. During scheduled office hours or by appointment, instructors and TAs will be available for live chat or video call on Microsoft Teams. You will be automatically added to a team for the course.

The instructor will hold office hours in the “General” channel, and TAs will hold office hours in the “TA Office” channel. The schedule for office hours can be found on Piazza Resources under the “Staff” tab.

Assignments

Homework

Homework assignments will be assigned every 1-2 weeks and must be completed individually. Each homework is due online via Canvas on the date scheduled on Canvas. (Please refer to the deadline on the actual assignment rather than the semester schedule, which is tentative and may not be updated if the assignment changes.)

Some aspects of the homework may be discussed with each other, but they should be completed individually, and your submitted work should be your own. Sharing of worked solutions will not be tolerated and will be considered cheating. Plagiarized solutions will receive a zero. Solutions with a very high degree of similarity with another past or current student’s will be considered plagiarism, and will be treated accordingly.

Exams

There will be mid-term and final exams during the semester. All exams will be completed online via Canvas on the dates scheduled on Canvas, and will replace a class meeting.

Project

Project guidelines will be shared on Piazza and reviewed in class around mid-semester. Use course tools to build something interesting of your choice. Projects may be done individually or in groups of up to 4.

Late work and grading

Late assignments will not typically be accepted. No-penalty extensions may be given on a case-by-case basis if requested at least 48 hours in advance of the due date with a reasonable justification.

Petitions for re-grades must be made in writing via Piazza private message to the instructor no later than 1 week after receiving the original grade. The petition must clearly explain why a re-grading is justified and why your answer(s) should be considered correct. The new grade may be lower than the original grade.

Before petitioning the instructor for a re-grade, students should first contact the grader (TA) to make sure they understand why they lost points.

Grade Scale

Students are required to get a B or above in the placement courses in order to progress into the core courses in the degree program. Students that do not achieve a B or better in the placement courses will be required to retake the courses.

The grade in this class is distributed as follows:

  • Homework: 50%
  • Exams: 50%
  • Project: <2%

Final grades will follow the following scale:

  • A : <= 100.0
  • A-: < 93.0
  • B+: < 90.0
  • B : < 87.0
  • B-: < 82.0
  • C+: < 80.0
  • C : < 77.0
  • C-: < 72.0
  • F: < 70.0

These scales are subject to change at the discretion of the instructor.