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pavlosprotopapas committed Nov 12, 2024
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Expand Up @@ -237,18 +237,26 @@ <h2 class="display-5">Courses</h2>
<div class="col-sm-6 col-md-4 mb-5">
<!-- Card Info -->
<div class="card h-100">
<img alt="Image Description" class="card-img-top" style="width:170pt; " src="assets/general/img/courses/chile_flag.jpg">
<img alt="Image Description" style="width:190pt; margin: 30px; margin-top: 40px;" src="assets/general/img/courses/sods_atfdds_header01.jpg">
<div class="card-body">

<h4 class="text-lh-sm" id='LSDSS'>Chile Data Science School</h4>
<p class="mb-0">La Serena Data Science School. <br>
This is a 10 day long course that I co-organize with <a href="https://www.linkedin.com/in/juan-pablo-cuevas-garcia-4b4a4a11/">Juan Pablo Cuevas</a>.
It is offered in La Serena, Chile and it is tailored for students in the social sciences and humanities.
The course covers data collection, data wrangling, visualization, statistical inference, and basic machine learning.
<h4 class="text-lh-sm" id='LSSDS'>La Serena School for Data Science (LSSDS), La Serena, Chile</h4>
<p class="mb-0">La Serena School for Data Science (LSSDS), La Serena, Chile. <br>
<a href="">LSSDS</a>.
I have been a regular faculty member at the La Serena School for
Data Science (LSSDS), an intensive 10-day program in La Serena, Chile,
focused on big data analysis, especially for applications in astronomy.
Since 2013, LSSDS has offered training in machine learning,
cloud computing, and statistical tools, with interdisciplinary lectures
and hands-on team projects using real data. This program is designed for
advanced undergraduates and early graduate students from the U.S., Chile,
Ecuador, and other Latin American countries. During my participation,
I have served as the lead faculty for machine learning and mentored
students on their projects.
</p>
</div>
<div class="card-footer">
<a class="font-weight-bold" href="https://harvard-iacs.github.io/CS109/">Know more here<i
<a class="font-weight-bold" href="https://lssds.aura-astronomy.org/winter_school/content/2024-la-serena-school-data-science">Know more here<i
class="fas fa-angle-right fa-sm ml-1"></i></a>
</div>
</div>
Expand All @@ -257,134 +265,62 @@ <h4 class="text-lh-sm" id='LSDSS'>Chile Data Science School</h4>
<div class="col-sm-6 col-md-4 mb-5">
<!-- Card Info -->
<div class="card h-100">
<img alt="Image Description" class="card-img-top" style="width:170pt; " src="assets/general/img/courses/italy_flag.jpg">
<img alt="Image Description" class="card-img-top" style="width:120pt; margin-left: 10px;" src="assets/general/img/courses/italy_flag.jpg">
<div class="card-body">
<h4 class="text-lh-sm" id='Italy'>Italy Data Science School</h4>
<p class="mb-0">Italy Data Science School.<br>
This is a 10 day long course that I co-organize with <a href="https://www.linkedin.com/in/gianluca-bontempi-91219913/">Gianluca Bontempi</a>.
It is offered in Milan, Italy and it is tailored for students in the social sciences and humanities.
The course covers data collection, data wrangling, visualization, statistical inference, and basic machine learning.
</p>
</div>
<div class="card-footer">
<a class="font-weight-bold" href="https://harvard-iacs.github.io/CS109/">Know more here<i
class="fas fa-angle-right fa-sm ml-1"></i></a>
<h4 class="text-lh-sm" id='Italy'>HPE Group, AI Workshop, Modena, Italy</h4>
<p class="mb-0">HPE Group, AI Workshop, Modena, Italy.<br>
A specialized course on sequential data and language models using deep
neural networks was designed and delivered to a cohort of 5 engineers at
<a href="https://www.hpe.eu/company/">HPE Group</a>, a leading provider of engineering solutions based in
Modena, Italy. This synchronous online course focused on advanced topics
such as recurrent neural networks, attention mechanisms, and transformer
architecture. Delivered over nine weeks, the course included nine lectures
and nine hands-on lab sessions, providing participants with a comprehensive
understanding of these cutting-edge technologies.
</p>
</div>

</div>
<!-- End Card Info -->
</div>
<div class="col-sm-6 col-md-4 mb-5">
<!-- Card Info -->
<div class="card h-100">
<img alt="Image Description" class="card-img-top" style="width:170pt; " src="assets/general/img/courses/AC215_logos.png">
<img alt="Image Description" style="width:110pt; margin-top: 30px; margin-left:20px;margin-bottom: 30px;" src="assets/general/img/courses/Flag_of_Rwanda.svg">
<div class="card-body">

<h4 class="text-lh-sm" id='AC215'>AC215</h4>
<p class="mb-0"> In today’s AI-driven landscape, building a deep learning model is just the beginning;
the real challenge lies in making it scalable, maintainable, and deployment-ready. AC215:
Productionizing AI (Machine Learning Operations) focuses on the entire ML operations workflow,
particularly for Large Language Models (LLMs). This course covers essentials like containerization,
cloud functions, data pipelines, and advanced training techniques, with a special emphasis on LLM
applications. You’ll learn to use LLM APIs, fine-tune models for specific tasks, and build scalable
applications, gaining the skills to deploy AI in real-world scenarios effectively.
<h4 class="text-lh-sm" id='Rwanda'>Rwanda Data Science School</h4>
<p class="mb-0">Rwanda Data Science School.<br>
The Rwanda Data Science School, held in Rwanda during summer 2019
and transitioned online in 2020-2021, was an intensive three-week program
introducing fundamental data science concepts. As one of two lead faculty members,
I helped develop and deliver curriculum to approximately 50 participants.
The program covered data scraping, basic machine learning models, and data visualization
through hands-on learning experiences. With three lectures per week, participants
gained practical skills in data analysis and visualization, preparing them for
real-world applications in data science.

<p class="mb-0">
​ </p>
</div>
<div class="card-footer">
<a class="font-weight-bold" href="https://harvard-iacs.github.io/2024-AC215/">Know more here<i
class="fas fa-angle-right fa-sm ml-1"></i></a>
</div>

</div>
<!-- End Card Info -->
</div>
<div class="col-sm-6 col-md-4 mb-5">
<!-- Card Info -->
<div class="card h-100">
<img alt="Image Description" style="width:170pt;" class="card-img-top" src="assets/general/img/courses/BEDROCK_logo.png">
<div class="card-body">

<h4 class="text-lh-sm">Bedrock Data Science</h4>
<p class="mb-0">The objective of this course is to provide fundamental understanding of math,
statistics & programming required to undertake a course in machine learning, data science or AI.
You will start with the basics of python, statistics, linear algebra, and calculus.

By the end of the course, you will have the tools and know the concepts needed to successfully
undertake a rigorous course in machine learning.

<h4>Course Topics:</h4>
<ol>
<li>Basic Python: Data types, data structures, functions</li>
<li>Advanced Python: Python Classes</li>
<li>Probability & Statistics</li>
<li>Linear Algebra & Calculus</li>
</ol>
</p>
</div>
<div class="card-footer">
<!--a class="font-weight-bold" href="">This course is offered only asynchronous during the summer. <i
class="fas fa-angle-right fa-sm ml-1"></i></a-->
This course is offered only online and asynchronously during the summer.
A version of this tailored for high schoolers is offered via <a href="https://www.system3.company">system3</a>.
</div>
</div>

<!-- End Card Info -->
</div>


<div class="col-sm-6 col-md-4 mb-5">
<!-- Card Info -->
<div class="card h-100">
<img alt="Image Description" class="card-img-top" style="width:170pt;" src="assets/general/img/courses/PINNS_logos.png">
<div class="card-body">

<h4 class="text-lh-sm">The PINNS course</h4>
<p class="mb-0">
In this course, we will first review differential equations and traditional numerical methods
to solve them. We will then delve into neural network approaches for solving differential equations, initial and boundary condition problems,
optimization methods, sampling techniques, error characterization, and transfer learning.
At the end of the course, students will be able to understand the challenges and advantages of PINNs,
know different approaches and compare them, and be able to implement and use existing
libraries to solve linear and non-linear ODE, a system of ODEs,
a system of linear and not linear-PDEs.
</div>
<div class="card-footer">
<!--a class="font-weight-bold" href=""><i
class="fas fa-angle-right fa-sm ml-1"></i></a-->
Offered Jan 2024. Check <a href="https://harvard-iacs.github.io/PINNS_course/">here</a> for info.
<br> I am currently working on publishing a book on Physics-Informed Neural Networks (PINNs), with an expected release in summer 2025.
</div>
</div>

<!-- End Card Info -->
</div>
<div class="col-sm-6 col-md-4 mb-5">
<!-- Card Info -->
<div class="card h-100">
<img alt="Image Description" class="card-img-top" style="width:170pt;" src="assets/general/img/courses/HarvardX_logos.png">
<div class="card-body">

<h4 class="text-lh-sm" id='HarvardX'>Introduction to Data Science with Python</h4>
<p class="mb-0">
<p >This course guides learners through essential data science techniques using Python,
covering regression, classification, and libraries like <code>sklearn</code> and <code>Pandas</code>.
Key ML concepts such as overfitting, regularization, and model evaluation are introduced,
providing a strong foundation in Python for advanced study in Machine Learning and AI.</p>
</p>
<a class="font-weight-bold" href="https://www.edx.org/learn/data-science/harvard-university-introduction-to-data-science-with-python">Know more here
<i class="fas fa-angle-right fa-sm ml-1" ></i></a>
<br><br>
<h4 class="text-lh-sm" id='HarvardX'>Machine Learning and AI with Python</h4>
<p>Focusing on decision-making through machine learning, this course introduces decision trees,
progressing to bagging, random forests, and gradient boosting.
Real-world cases help learners practice prediction, refine models, and address issues
like overfitting and bias, preparing them for complex decision-making using Python.</p>
<a class="font-weight-bold" href="https://www.edx.org/learn/machine-learning/harvard-university-machine-learning-and-ai-with-python?objectID=course-1dc2b7e4-21ab-4c39-9da0-1784d3321948&webview=false&campaign=Machine+Learning+and+AI+with+Python&source=edX&product_category=course&placement_url=https%3A%2F%2Fwww.edx.org%2Fbio%2Fpavlos-protopapas-3">Know more here
<i class="fas fa-angle-right fa-sm ml-1"></i></a>

</div>
<div class="card-footer">

</div>
</div>

<!-- End Card Info -->
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