Course overview

• We will cover the following topics in Artificial Intelligence
  • search
  • knowledge representation and reasoning
  • planning
  • reasoning under uncertainty
  • machine learning
• Instructor: Lirong Xia (xial@rpi.edu). Please call me Lirong and contact me via your RPI email
• TAs: Josh Kavner (joshuakavner@gmail.com), Ao Liu (aoliu.cs@gmail.com), Farhad Mohsin (farhadmohsinIII@gmail.com)
• Mentors: Elijah Burdo (burdoe@rpi.edu), Nana Baafi Boateng Agyekum (agyekn@rpi.edu), Mason duBoef (duboem@rpi.edu)
• Time: Monday Thursday 10:00 am-11:50 am
  • Lecture notes are here just for your reference. Please login with your RPI Box account after activation, and do not share the notes outside this class.
• Location: DARRIN 324
• Office hours (on Webex for now, see Piazza for more information):
  • Use Piazza first! You will get more timely help and everyone will benefit from your questions.
  • Lirong Xia: Mon noon-1pm
  • TAs and Mentors
    • Nana: Wed 11am-noon
    • Farhad: Wed noon-1pm
    • Elijah: Wed 1pm-2pm
    • Josh: Wed 2pm-3pm
    • Ao: Wed 3pm-4pm
    • Mason: Wed 4pm-5pm

Tentative schedule (subject to change)

Lecture notes and slides are only provided for your reference. Students are responsible for coming to class and taking their own notes.

Textbook for reference (not required)

Prerequisites

• A qualified student must have taken courses on
  1. Linear algebra
  2. Algorithm design (if you are an undergrade you must have taken CSCI-2300 Introduction to Algorithms. Students who did not pass CSCI-2300 will be removed from the class. If you are are graduate student you must have taken an equivalent class)
  3. Probability or statistics
• We will have programming assignments using Python, so you should be comfortable with programming

Objectives

• We hope to help with developing
  1. A taste of the field of AI
  2. Knowledge and techniques in major topics in AI
  3. Programming skills for implementing AI

General Class Policies

• Website and Announcements. We will make extensive use of piazza and the course website. You are responsible for checking the course website regularly for announcements and course materials, as well as your e-mail for communications related to the class.
• Lectures. Students are highly encouraged to attend all classes. You are responsible for all material covered and announcements made in lecture.
• Laptops and Electronic Devices. No laptops or other electronic devices are allowed in lecture, excpet that you use it for in-class polling. Even if you are diligently taking notes on your laptop, the bright screen and the activity is extremely distracting to the people behind you. Because of this, "movie theater" rules apply: no laptops, phones, or other devices with a screen on them should be out during lecture, except for in-class polling. After the polling, you mst turn it off. Students who continually disrupt the class will be asked to leave.

Grading

• Exam 1: 20%
• Exam 2: 20%.
• Exam 3: 20%.
• Projects: 25%
• Written Homeworks: 15%
• Must get average 65% in the exams to pass the course

Project assignments and written homeworks

• There will be 6 project assignments. Discussions are allowed but you must do the projects by yourself. If you benefit from a discussion with your friend or another work, you must acknowledge the author of the original work. Intro to AI is not a course on programming. If you need help on projects, please ask on Piazza or contact TAs and mentors. Lirong does not help with debugging projects, unfortunately.
• Evaluation: Your code will be autograded for technical correctness. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder. However, the correctness of your implementation -- not the autograder's judgements -- will be the final judge of your score. If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work.
• We also have some written homeworks. Again, discussions are allowed but you must do the homeworks by yourself.
• The bottom line is: Projects and HWs are best practices for the exams, you must get 65% in exams to pass the course.

Academic dishonesty and late policy

• Student-teacher relationships are based on trust. For example, students must trust that teachers have made appropriate decisions about the structure and content of the courses they teach, and teachers must trust that the assignments that students turn in are their own. Acts which violate this trust undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities defines various forms of Academic Dishonesty and you should make yourself familiar with these. In this class, you are allowed (and encouraged) to discuss homework and projects with other members of the class, and to formulate ideas together. However, everyone must write up their assignments completely separately, and include the names of everyone you discussed the assignment with. When working on projects, you may discuss general approaches to the problem, but you may not look at anyone else's code or show your code to them: all the actual programming must be done entirely on your own. Failure to write the solution to a homework or project completely on your own will be considered a breach of academic integrity. You may not copy (or near-copy) a solution from another, or use resources other than the class notes or the class textbook. Use of materials other than the class textbooks, including any material found on the Internet or material from previous versions of this course, is a clear breach of academic integrity and will be punished severely. No collaboration, or any electronic devices, is allowed during exams. Violating the above policy will result in the final grade being reduced by a letter and a 0-grade for the assignment for both parties. Depending on the circumstances, harsher penalties may be used, including a failing grade for the class.
• Due time. Project assignments must be turned in by midnight (11:59 pm) of the due date. Written homeworks are due before class.
• Late policy. Everyone has two days max for any single written homeworks or projects and in total no more than 6 late days for all HWs and projects (except in institute established illness or emergency---in that case please send me an official approval).
• Regrading. Problems about grading must be reported within one week when the grades are made available. This applies to all homeworks, projects, and exams.

Acknowledgements

Thanks Pieter Abbeel, Vincent Conitzer, John DeNero, Dan Klein, Malik Magdon-Ismail, Peter Sone for offering tremendous helps on developing the course!