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Undergraduate Academic Advising Instructions

All engineering students are required to seek advising from their faculty advisor prior to registering for the Spring 2023 semester. An advising hold will be placed on the record of each engineering student that will prevent them from registering until they speak with their faculty advisor. Students who intend to transfer into a different program must still meet with their faculty advisor. There will be no exceptions.

Advising procedures

Faculty will conduct their advising appointments either in person or remotely the week of October 24-28, 2022. Most faculty will conduct their advising appointments through Webex, Zoom, Skype, or in person, in their office, see faculty listing here.

Students will receive instructions from their faculty advisor with details on how they will conduct their advising appointments. Students who do not receive an e-mail by October 17, 2022 should email their advisor (see list here). Students who do not receive a response should email and await further instructions. Please include your name and UIN.

Advising appointment sign-up

Students may sign up for advising appointments from October 17-21, 2022. Students who do not sign up during this week will bear the responsibility of finding a mutually agreeable time with their advisor.

Verify who your advisor is before signing up (see advisor list here). Please keep in mind that you may be reassigned to another advisor if your advisor is unavailable this term.

Advisor Evaluations

In an effort to improve undergraduate academic advising, students will submit an advisor evaluation at the end of their advising session. Advisor evaluation surveys will be conducted online and are mandatory. The survey takes less than one minute. The results are anonymous. Your advisor will provide the link to the survey.

Preparing for your advising session

Print or download your uAchieve reports which shows you which classes you have, and which are missing. This is your guide for what to take, as long as you have the pre-requisites (can check by clicking on a course). You may also find it helpful to print or download the appropriate curriculum flowchart from the Student Resources page which, together with the uAchieve report, will guide you on what courses to take next.

Plan your schedule ahead of your advising session through the “Plan Ahead” tool:

  • Go to:
  • Sign in with your NetID
  • On the welcome page, click “XE Registration”
  • Click the “Plan Ahead” tool
  • Save a New Plan
  • From here, you can add/drop courses into your schedule to make sure there are no time conflicts.
  • Note: This is just a planner and does not sign you up for courses. You still need to sign up once registration opens.

Degree Audit reports (uAchieve)

Review your online degree audit report (found in the portal under uAchieve) prior to attending your faculty advising appointment and have it with you during your appointment. If you have questions about what is on your degree audit report or feel that it is inaccurate, please contact one of the engineering undergraduate advisors so that they can review the report and make any necessary changes. Send an email with your UIN and be as specific as possible about what you believe is wrong with your degree audit report so that they can better understand the problem and get back to you in a timely manner.

Course prerequisites

The College of Engineering has noticed that a large number of students are registering for courses without meeting the listed prerequisites. The prerequisites are listed for a reason, and it is expected that you know the material in these prerequisite courses before moving on to the next level. Instructor permission does not waive course prerequisites.

Students registered in courses for which they do not have the prerequisites will be dropped from these courses after the add/drop deadline and will not be able to add another course to their schedule. Courses dropped in this way will show up as a “W” on a student’s academic record. Students can view the prerequisites for courses in the UIC catalog.

Special topics for Spring 2023

The ECE department is offering several special topics courses in Spring 2023. For ECE undergraduates: all courses will count as ECE Technical Electives with a Request for Modification of Major Form.

Note: Undergraduates must also obtain instructor approval to register for a 500-level course. This is separate from the Major Modification Form (if the student requests technical elective credit).

ECE 394 Special Topics

Contextual Mid-Year Design
CRN: 46600 (2), W 3:00-4:50 p.m.
Instructor: Renata Revelo Alonso,
Prerequisites: This course is only open to ECE students who have taken between 8 to 41 credit hours in ECE-only courses. Please contact the instructor for approval.
Description: In this course, you will learn about the engineering design process through an introspection of various engineering inventions and innovations. Alongside learning of the engineering design process, you will also consider critical consciousness topics that affect design. In engineering design, often the technical considerations are prioritized; however, in this class, we will also prioritize learning of social, cultural, economic, and political considerations (i.e., critical consciousness topics) and understand how design is impacted by these. Throughout the semester, you will also work as a team on a design project working directly with a community organization, ENLACE, in Little Village. ENLACE is a non-profit organization focused on “improving the quality of life of Little Village residents.”

ECE 491 Special Topics

Intro to Neural Networks
CRN: 44042 (3), 44043 (4), MW 3:00-4:15 p.m.
Instructor: A. Enis Cetin,
Prerequisites: MATH 310, ECE 310 or equivalent and basic computer programming skills.
Description: This course provides an introduction to artificial neural networks and deep learning. Biophysical and mathematical models of neurons. Perceptron and its relation to the LMS algorithm. Parallel Computing and GPUs. Convolution neural networks, recurrent neural networks (LSTM and gated recurrent), and residual networks.

Information and Learning
CRN: 42096 (3), 42097 (4), TR 9:30-10:45 a.m.
Instructor: Mesrob Ohannessian,
Prerequisites: MATH 310; ECE 341 (or BIOE 339 or IE 342 or STAT 381)
Description: A first mathematical look at what information is, what it means to learn, and how the two are related. This course covers the basics of statistical inference and learning under the lens of information theory. This means that in addition to specific methods and algorithms that acquire knowledge from observations, this course also highlights the limits of what is possible and explains what it would take to reach them. All concepts are illustrated with applications. Topics covered: Statistical Inference, Entropy and Compression, Concentration Inequalities, Efficiency and Universality, PAC Learning, Model Complexity, Regularization, Mutual Information and Lower Bounds.

ECE 594 Special Topics

Smart Grid: Modern Distributed Power Systems
CRN: 42438, MW 3:00-4:15 p.m.
Instructor: Lina He,
Description: The increasing integration of renewable energy sources (RESs) into the distribution power systems has required the use of power electronic converters as interfaces to match RES characteristics with power system requirements, such as voltage, frequency, and harmonics. The purpose of this course is to provide students with the ability to model and analyze distributed power systems and explore technical challenges and solutions related to distribution power systems with RES integration.

Introduction to Quantum Information Science and Engineering
CRN: 44918, TR 2:00-3:15 p.m.
Instructor: Zizwe Chase, Chase,
Prerequisites: ECE-421 or PHYS-411 and MATH-310, or permission by the instructor
Course Description: This course will provide an introduction to the theory of quantum computing and information accessible to both graduate and advanced undergraduate students from all QIS-related backgrounds (ECE, PHYS, EP, Math, and CS).  Topics covered in this course include: 1) fundamental elements of quantum information processing (qubits, unitary transformations, density matrices, measurements); 2) entanglement 3) basic quantum algorithms 4) hands-on projects with state-of-the-art near-term intermediate scale quantum hardware and 5) intro to quantum error correction.

Helpful Links

Note: See the undergraduate Student Resources page for a full selection of links.