Join our Cloud HD Video Meeting
Instructor: Wookjin Choi
Office: Hunter McDaniel Building 302Sb
Email: [email protected]
Lecture: 6:30-7:50pm, MW
Office hours: MTW 1:00-3:00pm
Zoom: https://vsu.zoom.us/my/wchoi
Deep learning is introduced in this special topic course. The course begins with a review of Python and linear algebraic concepts, followed by discussions of basic machine learning and neural network concepts necessary to understand deep learning, such as convolutional neural networks and transfer learning.
Recommended Preparation: Python, Linear Algebra, Machine Learning
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Participation: 5**%** Midterm: 15**%** Quizzes: 30**%** Assignments: 20**%** Project: 30**%**
A 90%-100% B 80%-89% C 70%-79% D 60%-69% F < 60%
Exam: A midterm will be given. Make-up exams will not be permitted except under unusual circumstances with satisfactory written justification. Any student who misses an exam due to an unexcused absence will receive a grade of zero for that exam with no opportunity for make-up or substitution. University excused absences or those occurring with a good reason may be excused, and the exam must be taken within one week of the original scheduled date. In this case, arrangements should be made in advance.
Homework: Regular homework assignments will be assigned which will require significant effort outside of class. The assignments are designed to challenge you by requiring that you apply learned concepts to new situations. Assignments will likely prove to be the most important learning experience in the course. You should start each assignment days before it is due so that you will have time to get assistance should you get stuck. Solutions to homework assignments must be typeset either using a word processor or in plain ASCII text. No handwritten work (including scanned documents) will be accepted. You may include “hand-sketched" electronic illustrations if appropriate.
Submission: Solutions to homework assignments will be submitted via Blackboard. Presentation, readability, and clarity will count in grading. “If it looks like junk, it will be graded like junk.”
Academic Honesty Pledge: All homework solutions must contain the following pledge.
I (We), your name(s), affirm that the work I (we) have submitted is my (our) own. I (We) understand the answers that I (we) have submitted and can reproduce them independently if asked. None of the submitted work was directly copied from another source or individual."
Late Policy: Homework will be accepted up to two weeks late with a 10% penalty for each late week. After two weeks, the homework will not be accepted.