MACHINE LEARNING
http://kogan.rutgers.edu/ml-phd
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A Ph.D. course offered in the spring of 2024
Accounting and Information Systems
Rutgers Business School - Newark and New Brunswick
Rutgers University
Prof. Alexander Kogan
One Washington Park #924 (Newark), (973) 353-1064
kogan@rutgers.edu
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Location: |
Day: |
Time: |
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Newark Campus; 1WP-402 |
Thursday |
1:00 p.m. - 3:50 p.m. |
Overview: Many successful existing applications of machine learning include systems that analyze past sales data to predict customer behavior, identify fraudulent credit card transactions, and recognize faces or spoken speech. This course will focus on the theoretical foundations of machine learning and will cover supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, neural networks and deep learning, kernel machines, graphical models, Bayesian estimation, combining multiple learners, and design and analysis of machine learning experiments.
Required Textbook:
https://rutgers.instructure.com/courses/273250
For a student to gain access to our Canvas system, they must be enrolled and they must have a NETID. Once an enrolled student obtains a NETID, they will be added to the roster within 2 business days. Students should also check their email account in the system and if it is not correct, they need to update their official student record. Students who do not have a NETID, can create one online using this link: A student who is not familiar with Canvas can find introductory tutorial level information at:Rutgers Getting Started In Canvas for Students
Online Course and Hardware Specifications:
In the Spring of 2024 this course will be conducted face to face. As a backup option, in parallel with the face-to-face meetings, this class will also take place on Zoom. These Zoom sessions CANNOT BE used instead of face-to-face participation in this class meetings, and are intended only as an emergency backup option. To take part in these Zoom mettings, students MUST use their Rutgers Zoom account with the name netid@rutgers.edu. It will be IMPOSSIBLE to participate using personal Zoom accounts. To be able to participate successfully in this course students should have access to a stable Internet connection and a computer with the hardware specifications equal or exceeding the items listed below to make sure that this computer can capably support MS Office Professional and virtual computing environments.https://it.rutgers.edu/zoom/knowledgebase/how-to-create-your-rutgers-zoom-account/
Note that the activation of the Rutgers Zoom account will involve browsing to netid.rutgers.edu and selecting Service Activation as explained in the link. Please also note that accounts with the correct name CAN be created in other ways, and students may have done this already - such accounts will work for some purposes but will not provide proper access to required service during the semesters - you must seek assistance in removing such accounts if they prevent your creating the required account through Service Activation at netid.rutgers.edu.Instructions for signing into the activated Rutgers Zoom Account can be found at:
https://it.rutgers.edu/zoom/knowledgebase/how-do-i-log-into-my-zoom-account/
Regular attendance is essential and will be monitored. Absence for reasons of religious obligation shall not be counted for purposes of reporting. Be prompt for class. Classes will begin on time. Late arrivals disrupt class discussions. Please do NOT engage in online chat or any other online activities during the class. Make sure that your behavior shows respect to the instructor and to your classmates.
The penalties for cheating are severe. There is a university wide policy on academic integrity, which we will follow. It is not worth the risk of suspension from the university to cheat.
Rutgers University Academic Integrity Policy
Rutgers Business School Academic Integrity for Students
Rutgers Business School Student Code of Professional Conduct
Generative AI Usage Policy:
Use of generative AI tools such as ChatGPT or BARD is only permitted to help you brainstorm ideas and see examples. All material you submit must be your own.Bias Incident Reporting
Bias incidents: an act – either verbal, written, physical, or psychological that threatens or harms a person or group on the basis of actual or perceived race, religion, color, sex, age, sexual orientation, gender identity or expression, national origin, ancestry, disability, marital status, civil union status, domestic partnership status, atypical heredity or cellular blood trait, military service or veteran status. Bias incidents can be reported online at:
Rutgers University-Newark Incident Reporting Form
Coursework: The coursework includes attendance of lectures and participation in class discussions, writing a research paper and presenting it in class, completing a computational project in machine learning and submitting its results for evaluation, and taking the final exam that will be a review of a published machine learning paper.
Research Paper: Every student is required to write a research paper devoted to an important topic in machine learning. While it is expected that most students will choose a topic devoted to applications of machine learning in business, other topics may work as well. Every student is required to prepare a three page long proposal for the research paper, and submit this proposal on Canvas (as a Word or PDF document) for instructor's evaluation by March 7, 2024. The research paper should be presented during the last meeting of the class on April 25, 2024. Both the research paper (Word or PDF) and the presentation (PowerPoint) should also be submitted on Canvas before the last meeting of the class. The research articles to be covered in the research paper can be found in the following theoretical and applied journals publishing relevant articles:
https://www.libraries.rutgers.edu/find-access-and-request-library-materials/find-library-materials
The following online research tools can be useful in conducting bibliographic searches for your research paper:Computational Project: Every student is required to carry out a computational project focused on experimental comparison of several machine learning methods on different datasets. The computational experiment should compare at least FIVE different machine learning methods on at least FOUR different datasets. Every student is required to prepare a computational project proposal and submit this proposal (Word or PDF) on Canvas for instructor's evaluation by March 28, 2024. The proposal document must describe the machine learning methods and the datasets to be utilized in the experiment, as well as the proposed experimental methodology. The description of results of the computational experiment (Word or PDF) should be submitted on Canvas before the last meeting of the class on April 25, 2024. It is recommended that the computational experiments be conducted using the public domain Machine Learning software package called Weka (using the most recent book version - currently 3-8-5). Here are some useful Weka links:
http://archive.ics.uci.edu/ml/index.php
It is absolutely essential to start working on the research paper and the computational project as soon as possible.Final Exam: You will be given 24 hours starting at 1 PM on May 2, 2024 to write a critical review of a machine learning article. The article will become available at 1 PM on May 2, 2024 in the Final module on Canvas. The completed review has to be submitted by 1 PM on May 3, 2024. In preparation for this final, the students may wish to study Wiley's "Step by Step Guide to Reviewing a Manuscript":
Grading: The evaluations of your research paper, the computational project, and the final exam article review will be the basis for the course grade:
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40% |
Research Paper (1% for proposal) |
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30% |
Computational Project (1% for proposal) |
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30% |
Final Exam Article Review |
Communications and Course E-Mailing List:
The best way to contact me is via email. The course is supported by the RAMS e-mailing list ml-phd-list. The list membership is automatically synchronized with the current class roster. Make sure that your current e-mail address is available in the Rutgers online directory. To post a message to the list, e-mail it toAll the postings to this list are permanently archived and available from
https://rams.rutgers.edu/rams/archive.cgi
Please note that your postings should be appropriate for this course.
Every student is responsible for maintaining the current e-mail address in the Rutgers Online Directory. You can maintain your personal information at:https://personalinfo.rutgers.edu/pi/
Preliminary Schedule: