CSE 847 Syllabus (Fall 2025)

General Course Information

• Instructor: Jun Wu (wujun4@msu.edu)
• Time: Monday and Wednesday 12:40 PM - 2:00 PM
• Location: Wells Hall A324 (In Person)
• Office Hours: After each class. Additional discussions should be communicated in Piazza through public or private posts (https://piazza.com/msu/fall2025/cse847).
• TA: There is no TA for this class
• Prerequisite: CSE 840
  Basics of linear algebra, probability, algorithm design, and analysis, proficient programming in one of the following languages (Python, Matlab, or C++).
  We will have a screen quiz in the first class, which can be used as a reference on whether you have sufficient prerequisites for this class. (Note: The grade of the screen quiz will NOT be included in your final grade.)
• Course Textbook: The class has no main textbook. However, you may use materials from the following books as a reference. Lecture slides and additional reading materials will be provided on D2L.
  • Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006.
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition). Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Springer-Verlag, 2009.

Objective

In-depth understanding of machine learning and pattern recognition techniques with their applications.

Grading

• Project (1): 35%. 1 ∼ 3 students form a group to carry out a research project. Each team will give a short presentation at the end of the semester.
• Homework (3): 30%. There are 3 homework assignments in total, with equal weights. Homework assignments include both theoretical derivation and hands-on experiments with various learning algorithms. Each student should finish independently.
• Exams (2): 30%. There will be 2 in-person exams (1 midterm and 1 final exam), with equal weights. The final exam is scheduled on Tuesday, 12/09/2025 from 12:45 PM - 2:45 PM in Wells Hall A324.
• Class participation: 5%. Students are required to attend lectures and actively participate in class/online discussions. In-class quizzes will be used to track attendance.

Final grades will be assigned based on absolute percentage as follows:

Table 1: Final grades where [ ] denotes inclusion and ( ) denotes exclusion. The instructor reserves the right to move the thresholds down (but not up) based on the distribution of final percentages.

Class Project (35%)

• Project proposal (5%): due on 9/30/2025
  It should include the following details:
  • Project title
  • Team members: please specify the role of each member
  • Description of the problem you are trying to address
  • Preliminary plan (milestones)
  • Reference papers
• Project Intermediate Report (5%): due on 11/5/2025
  It should include the following details:
  • Project title: can be different from the proposal, but no change will be allowed for the final report
  • Abstract: summarize the studied problem and research progress
  • Data: summarize the (newly collected or public) data sets you have used so far and plan to use moving forward
  • Methodology: summarize the machine learning techniques you have used so far and plan to use moving forward
  • Team members: please summarize the contributions of each team member
  • Reference papers

• Group presentation (5%) on 12/1/2025 and 12/3/2025
  Each group will present their project during the final classes (including 10 minutes for presentation and 2 minutes for Q&A).

• Final project report (20%) due on 12/5/2025
  It should include the following details:
  • Project title
  • Abstract: A very brief summary of the studied problem, methods, and results
  • Introduction: An introduction of the problem, related work, methods, and results
  • Problem description: A detailed description of the problem setting you try to address in the project
  • Methodology: A detailed description of the methods used in the project
  • Results: A detailed description of your observations from the experiments
  • Conclusions and future work: A brief summary of the main contributions of the project and the lessons you learn from the project, as well as a list of some potential future work
  • Team members: Please summarize the contributions of each team member
  • Reference papers

Late Assignment Policy

The policy in general is that late assignments will NOT be accepted.

Attendance Policy

Attendance at all regularly scheduled class meetings is a requirement of this course. Students with unexcused absences for more than three consecutive class meetings prior to the middle of the term may be dropped from the course for non-attendance.

• REPORTING NON-ATTENDANCE. In compliance with federal regulations governing financial aid and veterans education benefits, instructors are required to report students who stop attending or who have never attended class. After the first week of classes, through the middle of the term of instruction, instructors who identify a non-attending student should notify their departmental office. Upon receiving a report of non-attendance, departmental representatives are encouraged to initiate an administrative drop.
• Attendance is defined as physical attendance or participation in an academically-related activity, including but not limited to the submission of an assignment, an examination, student-initiated correspondence related to class topics, participation in a study group or an online discussion. Instructors who do not take attendance may utilize key assessment points (e.g., projects, papers, mid-term exams, and discussions) as benchmarks for participation.
• DROP FOR NON-ATTENDANCE. Students may be dropped from a course for non-attendance by a departmental administrative drop after the fourth-class period, or the fifth class day of the term of instruction, whichever occurs first.

Other Policies

• Usage of Generative AI Tools: Students are not permitted to use generative AI tools (such as ChatGPT, GPT-based platforms, or similar AI writing assistants) for completing homework assignments, writing course projects, or during exams. Any direct use of AI-generated content in these tasks will be considered a violation of academic integrity.
  However, for course projects, students may use generative AI models as base models or components in machine learning experiments or research. This means you can build upon, modify, or analyze these models as part of your technical work. Nevertheless, all written reports and submissions must be entirely your own original writing and must not include AI-generated text.
• Spartan Code of Honor: Student leaders have recognized the challenging task of discouraging plagiarism from the academic community. The Associated Students of Michigan State University (ASMSU) is proud to be launching the Spartan Code of Honor academic pledge, focused on valuing academic integrity and honest work ethics at Michigan State University. The pledge reads as follows:
  As a Spartan, I will strive to uphold values of the highest ethical standard. I will practice honesty in my work, foster honesty in my peers, and take pride in knowing that honor is worth more than grades. I will carry these values beyond my time as a student at Michigan State University, continuing the endeavor to build personal integrity in all that I do.
  The Spartan Code of Honor academic pledge embodies the principles of integrity that every Spartan is required to uphold in their time as a student, and beyond. The academic pledge was crafted with the inspiration of existing individual college honor codes, establishing an overarching statement for the entire university. It was formally adopted by ASMSU on March 3, 2016, endorsed by Academic Governance on March 22, 2016, and recognized by the Provost, President, and Board of Trustees on April 15, 2016. Student conduct that is inconsistent with the academic pledge is addressed through existing policies, regulations, and ordinances governing academic honesty and integrity: Integrity of Scholarship and Grades, Student Rights and Responsibilities, and General Student Regulations.
• Academic Honesty: Article 2.3.3 of the Academic Freedom Report states that the student shares with the faculty the responsibility for maintaining the integrity of scholarship, grades, and professional standards. In addition, the CSE department adheres to the policies on academic honesty as specified in General Student Regulations 1.0, Protection of Scholarship and Grades; the all University Policy on Integrity of Scholarship and Grades; and Ordinance 17.00, Examinations. (See Spartan Life: Student Handbook and Resource Guide and/or the MSU Web site: www.msu.edu.) Therefore, unless authorized by your instructor, you are expected to complete all course assignments, including homework, lab work, quizzes, tests, and exams, without assistance from any source. You are expected to develop original work for this course; therefore, you may not submit coursework you completed for another course to satisfy the requirements for this course. Also, you are not authorized to use the www.allmsu.com website to complete any coursework in this course. Students who violate MSU academic integrity rules may receive a penalty grade, including a failing grade on the assignment or in the course. Contact your instructor if you are unsure about the appropriateness of your coursework. (See also the Academic Integrity webpage.)
• Limits to Confidentiality: Essays, journals, and other materials submitted for this class are generally considered confidential pursuant to the Universitys student record policies. However, students should be aware that University employees, including instructors, may not be able to maintain confidentiality when it conflicts with their responsibility to report certain issues to protect the health and safety of MSU community members and others. As the instructor, I must report the following information to the Department of Police and Public Safety if you share it with me: Suspected child abuse/neglect, even if this maltreatment happened when you were a child, Allegations of sexual assault or sexual harassment when they involve MSU students, faculty, or staff, and Credible threats of harm to oneself or to others. These reports will trigger contact from the Department of Police and Public Safety who will want to talk with you about the incident that you have shared. In almost all cases, it will be your decision whether you wish to speak with that individual. If you would like to talk about these events in a more confidential setting you are encouraged to make an appointment with the MSU Counseling Center.
• Accommodations for Students with Disabilities (from RCPD): Michigan State University is committed to providing equal opportunity for participation in all programs, services and activities. Requests for accommodations by persons with disabilities may be made by contacting the Resource Center for Persons with Disabilities at 517-884-RCPD or on the web at rcpd.msu.edu. Once your eligibility for an accommodation has been determined, you will be issued a Verified Individual Services Accommodation (VISA) form. Please present this form to me at the start of the term and/or two weeks prior to the accommodation date (test, project, etc.). Requests received after this date may not be honored.
• Disruptive Behavior: Article 2.III.B.4 of the Academic Freedom Report (AFR) for students at Michigan State University states: The student’s behavior in the classroom shall be conducive to the teaching and learning process for all concerned. Article 2.III.B.10 of the AFR states that the student has a right to scholarly relationships with faculty based on mutual trust and civility. General Student Regulation 5.02 states: No student shall … interfere with the functions and services of the University (for example, but not limited to, classes …) such that the function or service is obstructed or disrupted. Students whose conduct adversely affects the learning environment in this classroom may be subject to disciplinary action through the Student Judicial Affairs office.