From 66bd8574130b846ac5d037ea7cdd5ff895a82529 Mon Sep 17 00:00:00 2001 From: pavlosprotopapas Date: Sat, 9 Nov 2024 09:15:02 -0500 Subject: [PATCH] added pdf on philosophy and added other activities --- .DS_Store | Bin 14340 -> 14340 bytes assets/.DS_Store | Bin 10244 -> 10244 bytes assets/general/.DS_Store | Bin 8196 -> 8196 bytes assets/general/TeachingPhilosophy.pdf | Bin 0 -> 56405 bytes assets/general/img/.DS_Store | Bin 14340 -> 14340 bytes assets/general/img/courses/chile_flag.jpg | Bin 0 -> 14296 bytes assets/general/img/courses/italy_flag.jpg | Bin 0 -> 13879 bytes courses.html | 183 +++++++++++++++++++++- 8 files changed, 182 insertions(+), 1 deletion(-) create mode 100644 assets/general/TeachingPhilosophy.pdf create mode 100644 assets/general/img/courses/chile_flag.jpg create mode 100644 assets/general/img/courses/italy_flag.jpg diff --git a/.DS_Store b/.DS_Store index 0dfbb5e50db66cf1d483616d610daa971004f424..fc179c5f97841e42097324a1dbd43686117fedbe 100644 GIT binary patch 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Courses

+

Educational Activities and Philosophy

+ In this page, I outline my educational activities and philosophy. + These encompass not only the formal courses I teach but + workshops and less formal educational experiences that + lie outside traditional coursework. +
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+

+

Courses

Many of the members of the lab are in some way or another involved in the teaching of the following courses. @@ -208,8 +216,181 @@

Machine Learning and AI with Python

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Courses

+ Here I outline various teaching activities that I have been involved but do not fall under structured + courses. + +
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+ Image Description +
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Chile Data Science School

+

La Serena Data Science School.
+ This is a 10 day long course that I co-organize with Juan Pablo Cuevas. + 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. +

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+ Image Description +
+

Italy Data Science School

+

Italy Data Science School.
+ This is a 10 day long course that I co-organize with Gianluca Bontempi. + 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. +

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+ Image Description +
+ +

AC215

+

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. + +​

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+ Image Description +
+ +

Bedrock Data Science

+

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. + +

Course Topics:

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  1. Basic Python: Data types, data structures, functions
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  3. Advanced Python: Python Classes
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  5. Probability & Statistics
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  7. Linear Algebra & Calculus
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+ Image Description +
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The PINNS course

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+ 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. +

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+ Image Description +
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Introduction to Data Science with Python

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+

This course guides learners through essential data science techniques using Python, + covering regression, classification, and libraries like sklearn and Pandas. + 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.

+

+ Know more here + +

+

Machine Learning and AI with Python

+

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.

+ Know more here + + +
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