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Syllabus

Course Info

Professor: Dr Eren Gultepe
Class Times: TR, 9:30 am - 10:45 am
Lecture Location: Science East 2214

Office Hours: At my office, immediately after lecture or by appointment
Office: EB 3071
Email: egultep@siue.edu
Phone: (618) 650-2389

Course Description

Deep Learning is an advanced application of neural networks within the field of machine learning. This course motivates and covers the foundations of deep learning, alongside its current technological applications. This course will provide the knowledge and skillset to navigate the current landscape of artificial intelligence algorithms employed across various fields of study.

Course Objectives

To give an overview of the foundations and recent advances in neural net algorithms. The first 2/3 of the course focuses on supervised learning (“learning with a teacher”). The last 1/3 focuses on unsupervised learning (“learning without a teacher”) and reinforcement learning.

Course Organization

Slides, tutorials, homework, group project, and deadlines will be made available through the course website. Course submissons will be performed using Blackboard. Course announcements will be made through Blackboard and email.

When a topic is being covered, first a lecture session will be held and in the following class, a tutorial section in which the application of the topic in Python will be covered.

Course Prerequisites

The prerequisites are: MATH 152 - Calculus II, STAT 380 - Statistics for Applications

Assessment Criteria

The evaluations comprising a student’s grade in the course are:

Grading Scale

The grading scale (curve included) for the course is:

A ≥ 80%; B ≥ 65%; C ≥ 55%; D ≥ 50%

Topics Covered

The following is a tentative list of topics:

  1. Linear Models
  2. Multilayer Perceptrons
  3. Backpropagation
  4. Distributed Representations
  5. Automatic Differentiation
  6. Optimization
  7. Convolutional Networks
  8. Image Classification
  9. Generalization
  10. Recurrent Neural Nets
  11. Exploding and Vanishing Gradients
  12. Autoregressive and Reversible Models
  13. Variational Autoencoders
  14. Generative Adversarial Nets
  15. Bayesian Neural Nets

Late or missed assignments/exams

No makeup exams or assignments will be given. If you miss an exam or assignment, there must be documentation in writing, provided to me or to the department administrative assistant. I will still review whether you have exercised due diligence. Otherwise, you will receive 0 points for the missed exam or assignment.

Textbooks

There are no required textbooks for this course. The necessary reading material will be provided on the course website.

Academic Honesty

Plagiarism is the use of another person’s words or ideas without crediting that person. Plagiarism and cheating will not be tolerated and may lead to failure on an assignment, in the class, or dismissal from the University, per the SIUE academic dishonesty policy. Students are responsible for complying with University policies about academic honesty as stated in the University’s Student Academic Conduct Code.

Academic Integrity

Students are reminded that the expectations and academic standards outlined in the Student Academic Code (3C2) apply to all courses, field experiences and educational experiences at the University, regardless of modality or location. The full text of the policy can be found here: Student Academic Code.

Recordings of Class Content

Faculty recordings of lectures and/or other course materials are meant to facilitate student learning and to help facilitate a student catching up who has missed class due to illness. As such, students are reminded that the recording, as well as replicating or sharing of any course content and/or course materials without the express permission of the instructor of record, is not permitted, and may be considered a violation of the University’s Student Conduct Code (3C1), linked here: Student Conduct Code.

Accessibility

Students needing accommodations because of medical diagnosis or major life impairment will need to register with Accessible Campus Community & Equitable Student Support (ACCESS) and complete an intake process before accommodations will be given. The ACCESS office is located in the Student Success Center, Room 1270. You can also reach the office by e-mail at myaccess@siue.edu or by calling (618) 650-3726. For more information on policies, procedures, or necessary forms, please visit the ACCESS website at www.siue.edu/access.

COVID-19 Policies

Absences

Throughout the semester, all lectures slides, course assignments, and due dates will be posted on the course website. Also, all relevant course communications will be made through email and Blackboard announcements. Thus, no Zoom links of the lectures will be provided since all relevant course material will be available online.

Since you are encouraged to work in partners for your projects, for any short unplanned absences, your partners will be a vaulable resource (e.g., for obtaining any class notes). This will ensure a students’ continued progress in the course and if needed I will to help balance the circumstances regarding the absence.

In case of accommodations requested by the ACCESS office for medical diagonsis or major life impairment, the student’s absence will be accomadated on a case-by-case basis. Please see Accessibility or Accommodations)

At the discretion of the instructor, all material, assignments, and deadlines are subject to change with prior notice. It is your responsibility to stay in touch with your instructor, review the course site regularly, or communicate with other students, to adjust as needed if assignments or due dates change.