This 3-credit course will focus on modern and practical methods for deep learning. We will begin with a overview of simple classifiers, such as perceptrons and logistic regression, and move on to standard neural networks, convolutional neural networks, and elements of recurrent neural networks and transformers. The emphasis will be on the fundamentals and practical application, rather than an in-depth theoretical approach. While the majority of the applications discussed will focus on computer vision, we will also cover some natural language processing (NLP) applications. The programming components will be based on Python and its associated packages, such as Numpy and PyTorch. Students should have a strong background in Linear Algebra, Probability and Statistics, and Multivariate Calculus, and an ability to program in Python.
The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course. If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements.
Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Massachusetts Amherst. Academic dishonesty is prohibited in all programs of the University. Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty. Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty. Instructors should take reasonable steps to address academic misconduct. Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible. Instances of academic dishonesty not related to a specific course should be brought to the attention of the appropriate department Head or Chair. Since students are expected to be familiar with this policy and the commonly accepted standards of academic integrity, ignorance of such standards is not normally sufficient evidence of lack of intent (http://www.umass.edu/dean_students/codeofconduct/acadhonesty/).
Assignment #1: 15%
Assignment #2: 15%
Assignment #3: 15%
Final Project: 40%
There will be 3 homework assignments through the semester.
Many thanks to Jiajun Wu, Fei-Fei Li and Andrej Karpathy for graciously letting us use materials from the Stanford CS231n.