Details

• Days

  • • Monday, Wednesday, Friday

• Place

  • • Section 8395 Classroom [10:20 AM -11:15 AM ET]

    • Section 11087 Classroom [11:30 AM -12:25 PM ET]

    • Section 11086 Classroom [3:00 PM - 3:55 PM ET]

• Instructor

  • • • Jeniya Tabassum (tabassum.13@buckeyemail.osu.edu)

      • Office Hours: By appointment [Zoom]

• Teaching Assistants

  • • • Section 8395

        1. • Jesse He (he.1528@buckeyemail.osu.edu)

           • Office Hours: Tuesdays , Thursdays [2:30 PM - 3:30 PM ET ] [Zoom]

      • Section 11086

        1. • Jianan Hou (hou.388@buckeyemail.osu.edu)

           • Office Hours: Tuesdays , Thursdays [1:30 PM - 2:30 PM ET ] [Zoom]

      • Section 11087

        1. • Luis Morales (morales.247@buckeyemail.osu.edu)

           • Office Hours: Tuesdays , Thursdays [11:00 AM - 12:00 PM ET] [Zoom]

      • Project Supervision

        1. • Tingyang Xie (xie.963@buckeyemail.osu.edu)

             1. • Office Hours: Wednesday [ 4:00 PM - 5:00 PM ET] and Thursday [3:20 PM - 4:20 PM ET] [Zoom ]

           • Pranav Pitchala (pitchala.1@buckeyemail.osu.edu )

             1. • Office Hours: Monday [2:00 PM - 2:00 PM ET] [Zoom]

Topics

• AI terminology

• Fundamentals of AI problem solving

• Fundamentals of Knowledge Representation

• Fundamentals of Machine Learning

• Fundamental of Deep Learning

Suggested Textbooks

• Russell and Norvig, Artificial Intelligence: A Modern Approach, Third Edition [pdf]

• Tom Mitchell, Machine Learning. [pdf]

• Kevin Murphy Machine Learning: a Probabilistic Perspective [pdf]

• Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep learning. [pdf]

• Christopher M Bishop, Pattern recognition and machine learning. Springer, 2006. [pdf]

• Shai Shalev-Shwartz and Shai Ben-David, Understanding machine learning: From theory to algorithms. Cambridge university press, 2014. [pdf]

Grading

Grading will be based on the following parts:

• Participation and in-class Exercises (10%) : You will receive credit for asking and answering questions related to the homework on Piazza, engaging in-class discussion and participating in the in-class exercises.

• Homeworks (30%) : The homeworks will include different programming assignments. Homework should be submitted to Carmen by 11:59 pm on the day it is due (unless otherwise instructed).

  • • • Late HW penalty: -30%: 1 day late && -50%: 2 days late
        

• Final Project (30%): The final project is an open-ended assignment, with the goal of gaining experience applying the techniques presented in class to real-world datasets. Students should work in groups of 1-5 person. It is a good idea to discuss your planned project with the instructor to get feedback. The final project report should be 4 pages and is due on April 30. The report should describe the problem you are solving, what data is being used, the proposed technique you are applying, how you plan to evaluate your solution in addition to what baseline is used to compare against.

• Midterm (15%): Date: Friday, March 5, 2021 (using Carmen Quiz)

• Final (15%): Date: Thursday, April 29, 2021 (using Carmen Quiz)

Resources

• Carmen ( homework submission + grades )

  • • • Section 8395 Carmen

      • Section 11087 Carmen

      • Section 11086 Carmen
        

• Piazza ( for discussion ) [enroll here]
  

• OneDrive (for recorded class lectures) [check here]

Academic Integrity

• Any assignment or exam that you hand in must be your own work (with the exception of group projects). However, talking with others to better understand the material is strongly encouraged. Copying a solution or letting someone copy your solution is considered cheating. Code you hand in must be written by you, with the exception of any code provided as part of the assignment.
  

• Any collaboration during an exam is considered cheating. No part of the questions or answers to them may be reproduced, distributed, or transmitted in any form or by any means, including messaging, screenshotting, emailing, printing, photocopying, recording, or other electronic or mechanical methods, without the prior explicit and written permission. You must complete the exam on your own; i.e., no teamwork. If any violation of these rules are found, it will be considered as cheating.

Any student who is caught cheating will be reported to the Committee on Academic Misconduct. Please don't take a chance!! If you are having trouble understanding the material, let us know and we will be happy to help.

Feedback

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