COMP 193A/293A: Introduction to Deep Learning


Course Description | Administration | Learning Objectives | Course Material and Schedule | Grading and Attendance Policy | Exams | Assignment Guidelines | Academic Honesty | Accomodation for Students with Disabilities | Nondiscrimination Policy

Course Description

This course aims at introducing deep neural networks as an effective approach in constructing machine learning-based solutions. The course helps students in the design and development of neural networks in a given application domain, using hands-on approach. Topics include tensors, learning basics, artificial neural networks, and convolutional neural networks among other topics.

Prerequisites: COMP 053 and ECPE 170 with a "C-" or better.
When enrolling in this course, you should be relatively proficient in using Python. In addition to being a competent Python programmer, you should have an understanding of partial derivatives and gradients, probability, and using common data structures, e.g., lists, arrays, maps, classes, etc. We will use PyTorch to develop models and train them. Beforehand knowledge of PyTorch is not required.

Website: Syllabus, Canvas LMS

Credits: 3 units

Course Catalog: https://catalog.pacific.edu/search/?search=comp+293&caturl=%2F

Semester: Summer I, 2024




Administration

Instructor: Sepehr Amir-Mohammadian
Email:
Class time/location: Class is held in asynchronous online mode



Teaching Assistant:
Email:
Office hours:


Learning Objectives

The vision for this course is: What do I, as a computer scientist, need to understand the structure of deep neural networks and what are the different features by which I can customize these networks in a given project?

You will have many different opportunities to gain this knowledge through:

After taking this course, you should be able to:

University of the Pacific Core Competencies: This course reflects the following university-wide core competenceies in the undergraduate program:

Outcomes for COMP program: The assessment plan for this course comprises the following outcomes identified by ABET:

Collection of Work for Assessment: Student work may be retained to assess how course learning objectives are being met and for accreditation purposes.




Course Material

We will use the following textbook along with additional resources that are referred through the semester.

The slides, lectures, assignments, supplementary material, etc. will be provided through Canvas LMS.

Major topics that will be covered in the course are:




Grading and Attendance Policy

Grades for the course are assigned on the scale below:
A A- B+ B B- C+ C C- D+ D F
[93,100] [90,93) [87,90) [83,87) [80,83) [77,80) [73,77) [70,73) [67,70) [60,67) [0,60)

Final grades will be assigned based on several performance factors. These factors and their quantitative contribution to the final grade are as follows:

Attendance: This class is held online asynchronously. So, there are no attendance requirements.




Assignment Guidelines

Release and Submission

Late submission policy: Delliverables for quizzes and labs will not be accepted after the due date.
All assignments will be considered individual efforts unless otherwise specified, and will be treated as such under the Academic Honesty Policy.




Academic Honesty

The Honor Code at the University of the Pacific calls upon each student to exhibit a high degree of maturity, responsibility, and personal integrity. Students are expected to:

Violations will be referred to and investigated by the Office of Student Conduct and Community Standards. If a student is found responsible, it will be documented as part of her or his permanent academic record. A student may receive a range of penalties, including failure of an assignment, failure of the course, suspension, or dismissal from the University. The Academic Honesty Policy is located in Tiger Lore and online.

Course-specific Honor Code Policy: Engineering is generally a cooperative endeavor and collaborative learning can be a valuable experience for all involved. However, proper assessment (i.e., grading) requires that work be done by individuals. To balance these two requirements, the following policy will apply:

Marginal cases will be resolved by oral examination of the student(s) involved. If they each understand the material in the assignment, it will be considered honest collaboration. If they do not, then it will be considered academic dishonesty.

In many cases, it may be possible to identify reusable source code from textbooks, web sites or other resources that can help you with assignments. You are permitted to use such references provided that:

You are responsible for understanding the theory behind all algorithms or source code used, regardless of their source.


Accommodations for Students with Disabilities

If you are a student with a disability who requires accommodations, please visit pacific.edu/disabilities to contact the Office of Services for Students with Disabilities (SSD) for information on how to request accommodations while at Pacific.

The Office of Services for Students with Disabilities is located in the McCaffrey Center, Second Floor. Phone: 209-946-3221. Email: ssd@pacific.edu. Online: pacific.edu/disabilities


Nondiscrimination Policy

The University of the Pacific does not discriminate in the administration of any of its educational programs, admissions, scholarships, loans, athletics, or other University activities or programs on the basis of race, color, national and ethnic origin, handicap, sexual orientation or preference, sex or age.


The instructor reserves the right to change these policies and guidelines at any time, and students agree to abide by the most recent version of this syllabus.


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