Micro-credential in Artificial Intelligence
This page contains information and guidelines for students interested in pursuing the Micro-credential in Artificial Intelligence (MAI) from the Hewlett Packard Enterprise Data Science Institute (HPE DSI). The purpose of the MAI is to recognize the expertise gained by students during their studies in the areas of:
Core Courses
- 212 Scientific Computing with Python
- 312 Introduction to Deep Learning
- 313 Introduction to Large Language Models
- 411 Selected Topics in AI
Cost
- Free for active University of Houston System (UHS) students, staff or faculty
- $250/course badge for non-UH individuals
4 Badges = 1 Micro-credential
How it works: Register and complete four HPE DSI courses. These should include 4 core courses listed and the HPE DSI will automatically award you the Micro-credential in Artificial Intelligence. The Badge for the micro-credential will be awarded at the end of each semester.
Course Descriptions
To receive the Micro-credential badge, complete the courses listed below in any semester. These courses will neither affect your GPA nor appear in your transcripts. The description for each course can be found below:
Scientific Programming with Python
Python is an easy to learn, powerful programming language. It has efficient high-level data structures that make it suitable for rapid application development.
Topics covered in this session will include data types, conditional and loop statements, functions, input/output, modules, classes and exceptions.
Upon completion of this tutorial series, participants should understand existing scientific Python codes and be able to write their own simple Python applications.
This training session also introduces participants to scientific computing extensions of Python like NumPy for use in high-performance computing. Advanced Python libraries like regular expressions, SciPy, pandas, seaborn, scikit-learn, etc. for every day scientific computing will also be covered in the course.
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. Please note, this course requires familiarity with some basic concepts which are present in most programming languages: primitive and non-primitive data structures and operators, conditional and repeated execution, and working with libraries/modules. All programming work in this course will be done using Python, a beginner-friendly language.
Introduction to Large Language Models
In today's data-driven world, the ability to work with and understand human language is a critical skill. This course in Natural Language Processing (NLP) and Large Language Models offers a comprehensive exploration of the technologies that have revolutionized the way we interact with and analyze textual data.
In the technical aspects, students will also gain insights into the practical applications of NLP and large language models in various fields, including natural language information retrieval, sentiment analysis, named entity recognition, text classification, and data generation.
By the end of this course, students will have a good understanding of NLP and large language models, enabling them to apply these cutting-edge technologies to solve complex problems, drive innovation in their respective domains, and contribute to the responsible development of AI-powered applications. All programming work in this course will be done using Python, a beginner-friendly language.
This course introduces additional topics in AI, including ethics and aesthetics and discussions about how AI might change our relationship to knowledge. We will draw on current literature. In addition, selected speakers from industry will present the use of AI in industry. This course does not require any technical knowledge of AI.