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. 

Introduction to Deep Learning

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.

Selected Topics in AI

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. 

1. Enter the sign in credentials for your Badgr account in the Moodle Backpack page to request to connect.
2. If successful, the backpack status will change to “Verification pending”.
3. Check the email account you registered with Badgr for a verification email from HPE DSI.
4. Click on the verification link in the email to confirm and activate the connection of your Moodle backpack to your Badgr backpack.
5. If successful, the link will take you to your Moodle Backpack page and the status will change to "connected". 

1. Go to the “manage my badges” page, where you will see a list of all your earned HPE DSI Badges.
2. Click on the specific HPE DSI Badge you want to share; the link will take you to a details page about the Badge.
3. Click on the "Add to Backpack" button.
4. If successful, you will be taken back to the “manage my badges” page, which will now display an “Added badge to backpack” notice. You will also receive an email from Badgr noting that you have earned this Badge.

1. Sign in to your badgr.io account.
2. Select the Badge you want to share from the list.
3. Click on “Share” to generate a URL or link it to social platforms; Click on "Download" to save it on your computer device.