Artificial Intelligence

Computers are incredibly dumb. However, they can be programmed to appear incredibly clever using some fancy mathematics.
This introductory course on Artificial Intelligence covers:

  • search, planning, constraint-satisfaction problems and games
  • how to work with uncertainty in the environment
  • ML, reinforcement learning, NLP and recent developments
  • ethical questions about the use of AI

Instructor

Raj Venkat
Office: Meserve 303
r.venkatesaramani@northeastern.edu

Office Hours: Meserve 303, Thu 1:00 - 3:00 pm
To request appointments outside of office hours, click here.
If you decide to swing by on a whim, and my office door is open, feel free to bug me.

Syllabus

Click here to download the syllabus.

When & Where

Section A Section B
Lectures Cargill 097, MW
2:50 pm - 4:30 pm
Shillman 105, MWTh
10:30 am - 11:35 am
Class Forum Campuswire - Sec A
(Group join code - 2544)
Campuswire - Sec B
(Group join code - 7793)


Textbook & References

My course content is curated from several different sources, many of them not textbooks. However, you will find the following texts useful. Except for the first book in this list, the remaining are made freely available online by the respective authors.

Artificial Intelligence: A Modern Approach, Pearson
Artificial Intelligence 3E: Foundations of Computational Agents, Cambridge University Press, free online version
Mathematics for Machine Learning, Cambridge University Press, free online version
Dive into Deep Learning, Forthcoming at Cambridge University Press, free online version

Slides & Readings

Topic Readings
(Sec A) Intro, Course Logistics, History & Vocabulary
(Sec B) Intro, Course Logistics, History & Vocabulary
Notes: Introduction
AIMA: Ch. 1
ArtInt3E: Ch. 1
Search Notes: Search
Notes: Informed Search
AIMA: Ch. 3.1-3.6
ArtInt3E:Ch. 3.1-3.6
Informed Search Notes: Informed Search
AIMA: Ch. 3.1-3.6
ArtInt3E:Ch. 3.1-3.6
Local Search Notes: Local Search
AIMA: Ch. 4.1-4.2
ArtInt3E:Ch. 4.6-4.7
Gradient Descent (no slides) Notes: Gradient Descent
ArtInt3E:Ch. 4.8
Game Playing, Adversarial Search Notes: Games and Adversarial Search
AIMA: Ch. 5
ArtInt3E:Ch. 14.1-14.3
Adversarial Search - Dr. Mark Humphrys
Constraint Satisfaction Problems Notes: Constraint Satisfaction problems
AIMA: Ch. 6
ArtInt3E:Ch. 4.1-4.3
Reasoning with probability - Expectiminimax, Markov Models Seeing Theory - A Visual Intro to Probability
A Probability Primer - Dr. Scott Bierman
Notes: Expectiminimax
Notes: Markov Models
Markov Models. Ch. 8.1-8.9 [Stats 325 - Univ of Auckland, Dr. Rachel Fewster]
Hidden Markov Models Notes: Hidden Markov Models
AIMA: Ch 14.3
Hidden Markov Models, CMU, 2017
Markov Decision Processes Notes: Markov Decision Processes
AIMA: Ch. 17
ArtInt3E:Ch. 12.5
Intro to Reinforcement Learning AIMA: Ch. 22
ArtInt3E:Ch. 13
Notes: Brandon McKinzie
Machine Learning AIMA: Ch. 19, Ch. 20
ArtInt3E:Ch. 7
ArtInt3E:Ch. 10.3
Gradient Descent for Linear Regression
Maximum Margin Classifiers
Neural Networks AIMA: Ch. 21
ArtInt3E:Ch. 8
Notes: Mathematics of Neural Networks (Sec. 1, Sec. 2.1 and Sec. 2.4)
Deep Q Nets, Computer Vision, Deep RL AIMA: Ch. 22.4, 22.5
ArtInt3E:Ch. 13.9
Adversarial ML, Ethics TBA
NLP and LLMs (no slides) TBA

Grading

Grades will be based on the following split over course load:
  • Project: 40% (presentation, final report, modular and reusable code in GitHub repo)
  • Programming Assignments: 30% (4 assignments, NOT equally weighted)
  • Problem Sets: 20% (3 problem sets, NOT equally weighted)
  • Labs: 10% (grades awarded for completion)


  • Final grades will be assigned based on the following scale (note open and closed intervals):
    A [93, 100]
    A- [90, 93)
    B+ [87, 90)
    B [82, 87)
    B- [80, 82)
    C+ [77, 80)
    C [72, 77)
    C- [70, 72)
    F [0, 70)

    Natural rounding will be used, i.e., percentages $\ge x.5$ get rounded up to the next integer, $x + 1$ (92.5 becomes 93, 92.4 does not).
    I reserve the right to curve grades at the end of the semester. While not guaranteed, if a curve is applied, it will necessarily be in students’ favor.

Policies

Homework Submissions

  • Written submissions & labs must be submitted to Gradescope by 6:00 pm Eastern on the due date. Written submissions must be in PDF format.
  • All programming submissions must be uploaded according to assignment instructions by 6:00 pm Eastern on the due date.
  • All written solutions must be typed (i.e., no scans of handwritten assignments will be accepted). Scanned figures are only permitted where explicitly specified in the assignment instructions. Any such figures must be clear and perfectly legible.
  • The use of LaTeX is highly encouraged, but not mandatory. Overleaf is an excellent browser-based LaTeX editor with real-time compilation capabilities. Once you've created an account, you might find this template useful to get started on Overleaf. You'll need to create a copy of the template in your Overleaf projects to get edit access.
  • It is encouraged that you work with your classmates on the homework problems, but keep discussions at a conceptual level. If you do collaborate, you must write all solutions by yourself, in your own words, and are strictly forbidden from sharing any written solutions or code. You must list all of your collaborators on your submission. The TAs and instructors reserve the right to ask you to explain your solutions.
  • Grading All grades, including programming assignments, will be released via Gradescope.
  • Regrade Requests All regrade requests must be submitted within 1 week of receiving your grade. Requests for all submissions must be submitted from within Gradescope. Requests submitted via email will almost certainly be missed.

Policy on the use of Generative AI

  • For programming submissions, students may freely use any AI tool that is available to them, as long as they are appropriately cited (a code comment will suffice). I believe that it is imperative that students learn how to use these tools effectively and correctly, while also learning how to properly test and verify code generated by these tools. I recommend not using generative AI to assist with labs.
  • Students may not use generative AI to complete problem sets and written portions of homework assignments. These are intended to test students' ability to demonstrate mastery of techniques learnt in the course by a) presenting sound and rigorous theoretical analysis, and b) critically analyzing code. Any indication of the use of AI tools in the written submissions will constitute a violation of academic integrity.
  • The use of AI to rephrase sentences and improve writing clarity, etc. - while an acceptable use case - makes it difficult for instructors to discern whether the entire answer was AI-generated. Therefore, if AI is used in this manner, students are required to submit an additional appendix at the end of their submission with their corresponding originally written answers. The submission of such an appendix is aimed only at helping me understand the usage patterns of AI tools. Points will not be taken off for using AI to improve writing.

Late Policy

  • As a general rule, no late submissions are accepted. However, each student is given one 'freebie' - a no-questions-asked 3-day extension to a single homework of their choice. The freebie is intended to be a fallback in case of genuine emergencies where coordinating with the instructor may not be feasible. Unused days may not be carried forward to future assignments. Be wise in how you use this. The freebie may not be used for labs, presentations, or submissions related to the final project.
  • Once the freebie is used in either manner, I will generally not grant further extensions, except in the case of limited and verifiable emergency situations, or University and DRC-sanctioned accommodations. It is imperative that you communicate with me early on if circumstances permit. Timely submissions are the only way for me to get you timely feedback.
  • In case you have exhausted your freebie, and feel like you will be unable to submit an assignment in time, reach out to me. Depending on your circumstances, I may not give you an extension, but I will certainly offer you the right resources to help you make the best of your assignment. My only goal is to help you succeed.

Academic Integrity

  • Please familiarize yourself with Northeastern University's Academic Integrity Policy
  • Sharing of code in any form (including posting on Campuswire) is strictly forbidden. Searching for solutions online is okay, with appropriate citations in code comments. You may not ask TAs or the instructor to help debug code that was found online.
  • Any violation of academic integrity (as outlined by homework policies above) will result in an OSCCR report being filed against you.
  • Additional academic penalties, including but not restricted to failing the course without an option to withdraw may be levied against you at the discretion of the instructor.
  • Recognize that most violations are often easily avoided by simply acknowledging any difficulties you may be having with the course, and seeking help from your instructors in a timely fashion. We're here to help you learn.

Classroom Environment

To create and preserve a classroom atmosphere that optimizes teaching and learning, all participants share a responsibility in creating a civil and non-disruptive forum for the discussion of ideas. Students are expected to conduct themselves at all times in a manner that does not disrupt teaching or learning. Your comments to others should be constructive and free from harassing statements. You are encouraged to disagree with other students and the instructor, but such disagreements need to respectful and be based upon facts and documentation (rather than prejudices and personalities). The instructor reserves the right to interrupt conversations that deviate from these expectations. Repeated unprofessional or disrespectful conduct may result in a lower grade or more severe consequences. Part of the learning process in this course is respectful engagement of ideas with others.

TA Team & Office Hours

Campuswire, the platform we're using for 24/7 Q&A, also has built-in video chatrooms with automatic queue management, which will be used for any listed virtual office hours. TAs may offer a mix of in-person and hybrid office hours; timings and locations will be updated here.

Section A

Section B

Mrudula Acharya
acharya.mr@northeastern.edu
Office Hours: Mon & Fri,
8-10 am, Campuswire
Ashwin Bharadwaj
bharadwaj.ash@northeastern.edu
Office Hours: Mon & Wed 2-4 pm, EXP 7th floor common area
Shrey Desai
desay.shrey@northeastern.edu
Office Hours: Thu 8-10 pm,
Sun 8:30-10:30 am, Campuswire
Prajjwal Gupta
gupta.praj@northeastern.edu
Office Hours: Mon & Thu,
12 pm - 2 pm, Campuswire
Anurag Sanjay Ghosh
ghosh.anu@northeastern.edu
Office Hours: Tue & Fri,
9-11 am, Campuswire
Tarun Saxena
saxena.ta@northeastern.edu
Office Hours: Tue 10 am - 12 pm,
Thu 3-5 pm, Campuswire
Jai Surya Kode
kode.j@northeastern.edu
Office Hours: Tue 3-5 pm,
Hastings 209
Thu 3-5 pm, Hastings 207
Pratheesh
lnu.prat@northeastern.edu
Office Hours: Mon 3:30-5:30 pm,
Wed 3-5 pm, Campuswire
Aditya Ratan Jannali
jannali.a@northeastern.edu
Office Hours: Tue & Fri,
4-6 pm, Ryder 215
Kishore Sampath
sampath.ki@northeastern.edu
Office Hours: Mon & Fri,
10 am - 12 pm, Campuswire
Arjun Bhat
bhat.ar@northeastern.edu
Office Hours: Sat & Sun,
11 am - 1 pm, Ryder 460
Mukesh Javvaji
javvaji.m@northeastern.edu
Office Hours: Wed 5-7 pm, Ryder 154,
Sat 10 am - 12 pm, Campuswire

Campus Resources

Healthcare, Counseling, and Wellness

Your health and well-being are paramount, above any and all course deliverables. There is a wide range of support services on campus to ensure your success, and I encourage you to reach out to resources as appropriate.

University Health and Counseling Services
Find@Northeastern - 24/7 Mental Health Support
WeCare
Support Groups and Workshops


Title IX

  • Title IX of the Education Amendments of 1972 protects individuals from sex or gender-based discrimination, including discrimination based on gender-identity, in educational programs and activities that receive federal financial assistance. Northeastern’s Title IX Policy prohibits Prohibited Offenses, which are defined as sexual harassment, sexual assault, relationship or domestic violence, and stalking. The Title IX Policy applies to the entire community, including students, faculty and staff of all genders.
  • If you or someone you know has been a survivor of a Prohibited Offense, confidential support and guidance can be found through University Health and Counseling Services staff and the Center for Spiritual Dialogue and Service clergy members. By law, those employees are not required to report allegations of sex or gender-based discrimination to the University.
  • Alleged violations can be reported non-confidentially to the Title IX Coordinator within The Office for University Equity and Compliance by filling out the online Discrimination Complaint Form, emailing the OUEC (less secure) at: titleix@northeastern.edu and/or through NUPD (Emergency 617.373.3333; Non-Emergency 617.373.2121). Reporting Prohibited Offenses to NUPD does NOT commit the victim/affected party to future legal action.
  • Faculty members are considered “responsible employees” at Northeastern University, meaning they are required to report all allegations of sex or gender-based discrimination to the Title IX Coordinator. In case of an emergency, please call 911. Please visit the Title IX webpage for a complete list of reporting options and resources both on-campus and off-campus.


Disability Accessibility Services

Students with disabilities who wish to receive academic services and/or accommodations should visit Disability Accessibility Services at 20 Dodge Hall, or call 617-373-2675. If you have not already done so, please have the accommodations letter sent to your instructor early in the semester.