***This course is part of the Micro-credential in Data Science Program. Ask about this program at contact@hpedsi.uh.edu.
Machine learning is the science of developing statistical methods that quantify relationships within data. This branch of mathematics/computer science has seen explosive growth over the past decade as our ability to store and process digital data has dramatically increased. Prediction, classification, regression, and identification are the aims of learning from data. All of these problems are routinely performed in data analytics. To obtain an overview of the literature in learning-based methods and applications. To obtain an understanding of a variety of machine learning techniques for classification, regression, and prediction. To obtain the ability to implement and experiment with a wide range of machine learning algorithms in Python with examples. To apply: Unsupervised and Supervised learning and clustering concepts, Dimensional reduction, Kernels and kernel-based classifiers such as SVM, and Deep Learning algorithms. To understand and implement learning-based methods for the classification of images, signals, and features.
Prerequisites: Familiarity with Python Programming
Syllabus: Link to Syllabus
Dates: 23rd Jan 2024 - 27th Feb 2024
Time: Tue-Thur 2:30 PM - 4:00 PM
Instructor: Dr. Ioannis Konstantinidis
Teaching Assistant: Amala Yeruva (ayeruva@cougarnet.uh.edu)
Location: Microsoft Teams with CODE
Class Capacity:150
- 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 courses starting date to allow for proper registration.
Attendance Requirement:
Good standing on Attendance grade (>50%) would be required to see new course materials, homework assignments, and exams/projects as the course proceeds.
Also, for students interested in getting a badge or certificate for completing this course, an attendance grade of at least 12 hours of the class meeting sessions is required, to be qualified for passing the course and getting the badge.
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 prior to 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 link
https://store.mynsm.uh.edu/index.php?route=product/product&product_id=404&search=311
(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 don't wish to continue participating in the course, Please use the option "Unenroll me" available at the right-hand pane on the course webpage to unenroll from the course. If you have questions, 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: 15% cancellation fee applies.
Less than one week before the start date: 25% cancellation fee applies.
48 hours or less before class start: 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.