CSE 4392 - Special Topics: Neural Networks and Deep Learning
Spring 2023 - Course Information/Unofficial Syllabus

Course Description

Description of Course Content: This course offers an introduction to neural networks and deep learning. Topics include perceptrons, single-layer neural networks, multi-layer neural networks, Tensorflow and Keras, convolutional neural networks, transfer learning, deep learning methods for image classification, and sequential learning models for analyzing text. Auto-encoders and generative adversarial networks will be covered to some extent, as time permits. A strong programming and algorithmic background is assumed, as well as familiarity with linear algebra (vector and matrix operations).

Prerequisites: Admitted into an Engineering Professional Program. C or better in each of the following: CSE 3318 (Algorithms), CSE 3380 or MATH 3330 (Linear Algebra).

Student Learning Outcomes: After completing this course, students will be able to:

• Understand the concepts, and representation of the common neural network models and the most essential deep learning models and algorithms.
• Implement neural networks (from scratch).
• Use popular neural network libraries such as Tensorflow and Keras.
• Implement convolutional neural networks for image classification.
• Use transfer learning to improve classification accuracy.
• Implement and use neural networks for sequence learning, applicable to text.
• Understand the basic principles of auto-encoders.

Assignments

Attendance Policy

Class Participation

Student Conduct

• During lectures.
• During office hours.
• In any oral, written or electronic communication with the instructor and TAs.
• In assignment submissions.

Grading

The final semester score, calculated based on the percentages listed above, will be converted to letter grades based on the following scale:

• A: 90%
• B: 80%
• C: 70%
• D: 60%
• F: below 60%.

Any request for re-grading must be made within 5 days of receipt of that grade. Re-grading can lead to a higher or lower grade, depending on grading errors that are discovered.

There will be little or no extra credit. If there are extra credit opportunities, they will be included as part of the assignments, and they will be available to all students. There will be no make-up opportunities, and there will be no way for individual students to do extra work and improve their grade at the end of the semester.

IMPORTANT: It should be clear to every student that course grades will depend EXCLUSIVELY on the above grading criteria. Students should not request nor expect any other factor to be considered in computing the course grade. For example, factors that will NOT be considered are: need of a better grade to keep financial aid, to stay in the program, to qualify for a job offer, or to graduate. Students are expected to carefully monitor their own performance throughout the semester and seek guidance from available sources (including the instructor) if they are concerned about their performance and the course grade that they will earn. However, if the assignment scores are not good enough to warrant the desired grade at the end of the semester, there will be no other recourse for improving the grade.

Withdrawals

Expectations for Out-of-Class Study

University Policies and Services

Academic Integrity:

 
All students enrolled in this course are expected to adhere to the UT Arlington Honor Code:

I pledge, on my honor, to uphold UT Arlington's tradition of academic integrity, a tradition that values hard work and honest effort in the pursuit of academic excellence. I promise that I will submit only work that I personally create or contribute to group collaborations, and I will appropriately reference any work from other sources. I will follow the highest standards of integrity and uphold the spirit of the Honor Code.

Instructors may employ the Honor Code as they see fit in their courses, including (but not limited to) having students acknowledge the honor code as part of an examination or requiring students to incorporate the honor code into any work submitted. Per UT System Regents' Rule 50101, paragraph 2.2, suspected violations of university's standards for academic integrity (including the Honor Code) will be referred to the Office of Student Conduct. Violators will be disciplined in accordance with University policy, which may result in the student's suspension or expulsion from the University.

Student Support Services:

 
UT Arlington provides a variety of resources and programs designed to help students develop academic skills, deal with personal situations, and better understand concepts and information related to their courses. Resources include tutoring, major-based learning centers, developmental education, advising and mentoring, personal counseling, and federally funded programs. For individualized referrals, students may visit the reception desk at University College (Ransom Hall), call the Maverick Resource Hotline at 817-272-6107, send a message to resources@uta.edu, or view the information at www.uta.edu/resources.

Course Schedule

Lectures Schedule

Assignments Schedule