Syllabus (Spring 2025)

• Logistics
  • Course time and venue
  • Office hours
  • Recommended text books and references
  • Required tools and software
• Student Responsibilities
  • Knowledge and content scope
  • Responsibilities
  • Expectations
• Grading
• DRES
• Academic Integrity
• Absences and Late Work Policy
  • Excused absense
  • Late work
  • Penalty-free late days

Logistics

Course time and venue

• Venue: English Building (EB) 44.
  • Lectures: 11:00 AM - 12:20 PM Tuesdays
  • Lab: 11:00 AM - 12:20 PM PM Thursdays

Office hours

• Assistant Professor Yan Tang
  • 4:30-5:30 PM Thursdays @ LCLB, Room 4023.
• Teaching Assistant Mingyue Huo:
  • Online: 3:00 - 4:00 PM Wednesdays
  • Zoom link

Recommended textbooks and references

• Aurélien Géron, “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, Third Edition”, O’Reilly, 2022.

• Sebastian Raschka, Vahid Mirjalili, “Python Machine Learning, Third Edition”, Packt, 2019.

• José Unpingco, “Python for Probability, Statistics, and Machine Learning, Second Edition”, Springer, 2019.

• Guido van Rossum, et al., “The Python Language Reference”. Available for free in multiple formats

  This book is the authoritative reference manual for Python 3.

• Scott Chacon and Ben Straub, “Pro Git, Second Edition”, Apress, 2014. Available for free in multiple formats

  As a quick reference for the use of git, this book covers most of the hands-on commands for common operations on Git.

Required tools and software

• Students are expected to have their own computers for after-class exercises and assignments.

• The programming language used in this course is Python of version 3.

  Students are responsible for installing the programming environment on their own computers. It is recommended to install Python and relevant modules via Anaconda Distribution.

• Git software and Github account for assignment deployment and submission

  Git is a version control system that records changes to a file or set of files over time. For Mac users, the git software is built in the macOS, so nothing needs to be done. For Windows users, the binary installer can be downloaded from Git. While both 32- and 64-bit versions are available for downloading, Windows systems of contemporary versions should fully support the latter.

  Students are required to register a free GitHub account at https://github.com, where exercises and assignments will be submitted for grading during the course.

  All Github repositories associated with assignments in this course will be stored on the course Github account until the same course is offered again next year, at which point all the repositories from the previous year will be deleted. Therefore, if needed, it is students’ responsibility for keeping a copy of their own repositories before they are removed.

Student Responsibilities

Knowledge and content scope

• Some of the knowledge/contents involved in lab sessions may not be covered in lectures.
• Some of the knowledge/contents involved in homework assignments may not be covered in lectures or lab sessions.

Responsibilities

• Students are responsible for all of the material covered in class.
• Students are responsible for all of the material covered in lab sessions.
• Students are responsible for all of the material covered in homework.
• Students are responsible for all of the material covered in public Campuswire posts. This includes clarifications to homework instructions.
• Students are responsible for informing the instructors of any issues that they have encountered during the course promptly.
• Students are responsible for the originality of their solutions/code.

Expectations

• Students are expected to attend the entirety of every lecture and lab session and actively participate in the class.
• Students are expected to develop self-learning and problem-solving abilities utilising all possible resources, e.g. office hours, textbooks, online content and discussion with peers.
• Students are expected to actively follow along and practice the examples presented in class.
• Students are encouraged to conduct discussions with classmates.

Grading

Students will be assessed on the extent to which they have attained the learning goals & outcomes. This assessment will be primarily hands-on, assessed through a combination of practical exercises in a computer lab and homework assignments. Assessments on theories in a written form however will make up a relatively small portion of the final grade.

Grades will be assessed on a 100-point scale:

• Lecture attendance, lab exercises and theory assessments will make up 30% of the overall grade:
  • Attendance: 10%
  • Mid-term assessment: 10%
  • End-term assessment: 10%
• Lab exercises, after-class programming and scripting assignments will make 70% of the overall grade:
  • Lab exercise: 30%
  • Homework: 40%

The final letter grade for this course will be converted from the numeric grade using the following table:

Numerical grade		Letter grade
98.00 - 100.00		A+
92.00 - 97.99		A
90.00 - 91.99		A-
88.00 - 89.99		B+
82.00 - 87.99		B
80.00 - 81.99		B-
78.00 - 79.99		C+
72.00 - 77.99		C
70.00 - 71.99		C-
68.00 - 69.99		D+
62.00 - 67.99		D
60.00 - 61.99		D-
< 60.00		F

DRES

If a student has a disability or condition that requires special consideration and accommodation, the student is expected to inform the instructor and the TA by email no later than the beginning of the fourth week. The message in the email should:

• Include a scanned attachment of the requisite letter from the University Division of Disability Resources and Educational Services (DRES).
• Include a detailed description of the accommodations that the student is requesting for this class.

To contact DRES students may visit 1207 S. Oak St., Champaign, call 333-4603 (V/TTY), or email disability@illinois.edu.

Academic Integrity

This course follows the University of Illinois Student Code regarding Academic Integrity. The College of Liberal Arts and Sciences also has an excellent web page on the topic. You are expected to read these resources prior to the second day of class, and to understand your responsibilities with regard to Academic Integrity.

Students’ Quick Reference Guide to Academic Integrity states that it is students’ “responsibility to refrain from infractions of academic integrity, from conduct that may lead to suspicion of such infractions, and from conduct that aids others in such infractions. “I did not know” is not an excuse.”

All work submitted for this class must be solely each student’s own. Violations of Academic Integrity include, but are not limited to, copying (from e.g. peer classmates, online sources, and AI tools), cheating, and unapproved collaboration. There is zero tolerance for any form of violation. Any violation of academic integrity, even unintentional, may result in penalties as stated in the Student Code and the course syllabus.

Should an incident occur in which a student is believed to have violated academic integrity, the instructor(s) will initiate the procedures outlined in the Illinois Academic Integrity Policy via Faculty Academic Integrity Reporting (FAIR), the system for reporting academic integrity allegations and violations.

Absences and Late Work Policy

Excused absence

If a student will be absent from class for any reason, the student is expected to inform the course instructor ahead of time should the student wish to receive penalty-free credit:

• Each absence must be separately reported ahead of time via a private post on Campuswire to the instructor
• Each Campuswire post must be filed under the folder “Excused absence”
• Each Campuswire post must list the date of the absence
• Each Campuswire post must list the reason for the absence
• Each Campuswire post must mention whether or not makeup credit is being requested

No penalty-free credit will be granted if the procedure is not strictly followed. However, penalty-free credits from excused absence can be no more than 30% of the total Attendance credits presented above. Once penalty-free credits reach the limit, no more credit will be granted for any form of absence.

Late work

In-class exercises during a lab session must be turned in before 12:00:00 the first Friday after the lab session takes place. The deadline for a homework assignment will be set in its description when being announced. Lab exercises and homework assignments turned in late will be docked 15 percentage points per day late.

For some or all lab exercises and homework assignments, reference solutions will be presented to the class after the deadlines. The solutions will typically be presented during the class on the Thursday after an assignment is due. Under no circumstances will late work be accepted after the solution has been presented to the class.

Penalty-free late days

It is understood that illness and other extraordinary events do occur from time to time. In order to accommodate such extraordinary events, students will be allotted a budget of four (4) penalty-free late days for which no late penalty will be assessed for lab exercises and homework assignments. Penalty-free late days are intended to accommodate unforeseeable extraordinary events, not poor planning or poor time management.

If a student wishes to make use of a penalty-free late day, the student must do all of the following prior to the current assignment deadline:

• Post a private Campuswire message to the instructor:
  • The message must be filed under the Campuswire category for that homework assignment.
  • The title of the message must be “Penalty-free late day (X of 4)”, where X is replaced by the number 1, 2, 3 or 4.
  • In the body of the message, the student must explicitly request the use of a penalty-free late day.
• In the student’s git repository for the assignment, the student must note the request in the appropriate file, check in the change, and push the change to the appropriate remote repository.

Only when all of these steps have been taken prior to the deadline will a penalty-free late day be applied. If a student wishes to make use of more than one penalty-free late day per assignment, all of the above steps must be performed separately for each penalty-free late day.

Penalty-free late days cannot be used to extend any deadline beyond the last regular day of class for the semester.

Penalty-free late days cannot be used to turn in work after the solution has been presented to the students.

Penalty-free late days do not apply to mid- and end-term assessments.