Machine Learning, Spring 2024


Course: COMP 379-001 / COMP 479-001 Machine Learning
Level: Undergraduate and Graduate
Instructor: Daniel Moreira (

Lectures: THR, 4:15 to 6:45 PM, 123 Institute of Environmental Sustainability
Office Hours: FRI, 8 AM to 5 PM, 310 Doyle Center or Zoom, by appointment


“Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.”

– Arthur Samuel, 1959

Even though Samuel’s definition is now more than six decades old, it still holds true. With the recent advances in computer processing power, memory, and storage, machine learning has stressed its learn-by-example data-driven aspect, and is available — commonly as a black box — to everyone.

Annotated high-quality datasets can be easily harnessed to train a multitude of models to solve very specific problems under the different paradigms of supervised, unsupervised, hybrid (e.g., semi-supervised and self-supervised), and reinforcement learning. This course will cover these paradigms, trying to establish a balance between theory and practice. While students will be exposed to the theories that fight the black-box and irresponsible usage of machine learning, hands-on activities leveraging real-world data will prepare them for industrial, academic, and societal needs.

Let’s learn how the machines learn!

Requirements to attend this course are basic programming skills (especially Python), data structures, math fundamentals (such as linear algebra and calculus), and probability and statistics. This course and its materials are also available in Sakai.

Schedule (Tentative)

  • 01/18 - Syllabus and Intro
  • 01/25 - Data-driven Aspects
  • 02/01 - Supervised Learning I
  • 02/08 - Supervised Learning II
  • 02/15 - Supervised Learning III
  • 02/22 - Supervised Learning IV
  • 02/29 - Supervised Learning V
  • 03/07 - Spring Break, no classes.
  • 03/14 - Ensemble Learning.
  • 03/21 - Unsupervised Learning.
  • 03/28 - Easter Break, no classes.
  • 04/04 - Neural Networks I.
  • 04/11 - Neural Networks II.
  • 04/18 - Hybrid and Reinforcement Learning.
  • 04/25 - Project Presentations.
  • 05/02 - Final Exam.

Important Dates

  • 02/15 - Definition of project groups.
  • 02/29 - Definition of project topics.
  • 03/07 - Spring Break.
  • 03/14 - Midterm Exam.
  • 03/21 - Definition of project plan.
  • 03/28 - Easter Break.
  • 04/11 - Report of project status.
  • 04/18 - Graduate students’ lectures.
  • 04/25 - Project presentations.
  • 05/02 - Final Exam.


Concept  Interval (%)  Concept  Interval (%)  Concept  Interval (%)  Concept  Interval (%)
A [96, 100) B+ [88, 92) C+ [76, 80) D+ [64, 68)
A- [92, 96) B [84, 88) C [72, 76) D [60, 64)
B- [80, 84) C- [68, 72) F (0, 60)


Undergraduate   Graduate
Assignments (4)   30% 25%
Exams (2) 30% 25%
Project 30% 25%
Participation 10% 10%
Topic Lecture N.A. 15%
On the News +1% (extra) +1% (extra)


  • Soon.

Late Policy
Deduction of 10% of the maximum possible grade for each day of delay.


  • Midterm Exam, 03/14.
  • Final Exam, 05/02.


  • Written report and presentation, work alone or in groups.
  • More soon.


  • Class Attendance: every presence counts.
  • Today-I-missed Statements: every submission counts.

Today-I-missed Statements

After every attended class, each student will have to submit (through Sakai) a short paragraph answering one of the following:

  1. What is your biggest question after class? OR
  2. What was the most interesting point you learned today?

Inspired by Dr. Sandra Avila.

Today I missed…

Oopsie Cards

Each student has two “Oopsie” Cards, which will allow them to avoid losing points because of late delivered work. The cards are not valid to dismiss or postpone exams, topic lectures (graduate students), or final project dates. Students may use their cards at their own discretion, as long as they clearly communicate the instructor.

Life happens, be wise.

Oopsie card.

ML on the News

  • Soon.

Academic Integrity

Students are expected to adhere to the LUC statements on academic integrity available at These policies fully apply to this course. The penalty for task-wise academic misconduct is losing all the task’s points. Multiple events of misconduct will incur in failing the entire course (with an F grade). All cases of academic misconduct will be reported to the proper department offices. Lastly, students are not allowed to use AI assisted technology (such as ChatGPT) along the entirety of the course, unless explicitly authorized by the instructor.


Students who have disabilities and wish to request academic accommodations are advised to contact the Services for Students With Disabilities (SSWD) office at 773-508-3700 or as soon as possible. The SSWD office will provide accommodation letters that, once shared with the instructor, will be fully accommodated as per the terms of their content with no further questions.

Daniel Moreira
Daniel Moreira
Assistant Professor of Computer Science

Computer scientist with interests in (but not limited to) Computer Vision, Machine Learning, Media Forensics, and Biometrics.