Deep learning refers to AI/ML techniques that utilize multilayer (deep) artificial neural networks. This branch of data science has seen exponential improvements in performance as our ability to collect, store, and process digital data has dramatically increased. Prediction, classification, regression, and identification of semi/unstructured data are areas where deep learning techniques exhibit a significant comparative advantage.
Course aims:
- To obtain an overview of the literature on deep learning methods and applications.
- To obtain an understanding of a variety of deep learning techniques for classification, regression, and prediction.
- To obtain the ability to implement and experiment with a wide range of deep learning algorithms in Python with examples: e.g., convolutional networks (CNN), auto-encoders, generative adversarial networks (GAN), transformers/attention (BERT)
- To understand and implement deep learning-based methods for the classification of semi/unstructured data such as images or language.
Prerequisites: HPE DSI 311 – Introduction to Machine Learning.
Date: 18th March 2025 - 22nd April 2025
Time: Mon Wed 2:30 PM - 4:00 PM
Instructors: Dr. Ioannis Konstantinidis
Class Capacity: 150
Location: online
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HPE DSI’s courses are free for UH System students from outside the Main Campus. However, these individuals must enroll in our courses at least three weeks before the course's starting date to allow for proper registration.
Clear Lake, Victoria, Downtown, Sugar Land, or Katy campuses
Attendees who are current UHS students, staff, or faculty from the Clear Lake, Victoria, Downtown, Sugar Land, or Katy campuses MUST fill in this credentials form at least three(3) weeks before the course start date. This will allow adequate time to process your course access credentials before the first class.
Current UHS (all campuses) Student, Staff, or Faculty Registration Fee: Free.
UH Alumni and Non-UH Affiliates
Attendees who are not current UHS students, staff, or faculty will need to make a payment for the course before its commencement.
UH Alumni Registration Fee: $250.00.
Non-UH Affiliate Registration Fee: $250.00.
** Non-UH affiliates and alumni registering for the HPE DSI classes MUST:
(1) complete the payment of the course fees ($250.00 per course) at least three(3) weeks before course commencement using this payments link.
(2) fill in this credentials form at least three(3) weeks before the course start date. This will allow adequate time to process your course access credentials before the first class.
If you need to cancel a course for any reason, e-mail us at contact@hpedsi.uh.edu
** The following refund schedule applies:
More than three weeks before the start date: A cancellation fee of 10% of the total workshop cost applies.
Less than three weeks before the start date: A 15% cancellation fee applies.
Less than one week before the start date: A 25% cancellation fee applies.
48 hours or less before class starts: No refund is available.
All deposits and payments made are non-transferable. The HPE DSI cannot provide refunds, transfer payments, or offer makeup sessions for classes a student might miss, for any reason. If the HPE DSI has to cancel a workshop, you will receive a full refund.
Copyright protection: The course materials and online lecture videos posted on Teams are only meant to be used within this course and should not be distributed.
The University of Houston Academic Honesty Policy applies: http://www.uh.edu/provost/policies/honesty