Python Course: Machine Learning, Optimization and Applications (7th Edition)

Pre-registration period: Until October 20, 2024

Registration Period: From October 1st to 20th, 2024

Course start date: November 20, 2024

End of course: January 30, 2025

Credits: 10 ECTS

Price: €560 (taxes included)

More information: https://cfp.us.es/cursos/fc/python-machine-learning-optimizacion-y-aplicaciones/4724/ storal@us.es , d.gutierrez.reina@gmail.com

The continuing education course in Python: Machine Learning, Optimization, and Applications (8th Edition), worth 10 ECTS credits and available in both in-person and online formats, is offered by the Continuing Education Center of the University of Seville. Classes will be broadcast synchronously and recorded, making them available to students through the University of Seville's Virtual Learning platform.

This course offers a comprehensive overview of programming Machine/Deep Learning algorithms and optimization using the Python programming language. The course is structured into five modules:

Module 1 covers the basics of Python programming and the numpy, matplotlib, pandas, and scipy modules. Only basic programming knowledge is required as a prerequisite; no prior experience in Python is necessary, as this module starts from scratch.

In Module 2, students will learn the fundamentals of machine learning techniques, applying them to regressions, classifiers, and clustering. The classes include theoretical explanations and practical application scripts in Python using the scikit-learn library.

Module 3 covers various metaheuristic optimization methods using Python's DEAP module: local search methods based on paths and global search methods based on populations. It also includes a section on reinforcement learning using Python's Gym library.

In Module 4, students will learn the fundamentals of deep learning in Keras and TensorFlow, and how to apply these techniques to solve real-world problems. A wide variety of neural network architectures, such as dense networks, convolutional networks, recurrent networks, and deep reinforcement learning, will be studied from both theoretical and practical perspectives.

Module 5 is dedicated to applications. Over 5 sessions and in self-contained classes, several academic and industry professionals will demonstrate application examples from the previous modules.

The course will be taught by experts in the field of machine learning and optimization, who will share their extensive knowledge and experience with the students, and is aimed at

Students passionate about technology and eager to make the most of the opportunities offered by machine learning and optimization. The skills acquired in this course are highly valued in the industry and can open doors to a wide range of career opportunities.

Student selection criteria based on order of pre-registration. A university degree is not required to access the course.

Pre-registration period: Until October 20, 2024

Registration Period: From October 1st to 20th, 2024

Course start date: November 20, 2024

End of course: January 30, 2025

Credits: 10 ECTS

Price: €560 (taxes included)

More information: https://cfp.us.es/cursos/fc/python-machine-learning-optimizacion-y-aplicaciones/4724/ storal@us.es , d.gutierrez.reina@gmail.com

The continuing education course in Python: Machine Learning, Optimization, and Applications (8th Edition), worth 10 ECTS credits and available in both in-person and online formats, is offered by the Continuing Education Center of the University of Seville. Classes will be broadcast synchronously and recorded, making them available to students through the University of Seville's Virtual Learning platform.

This course offers a comprehensive overview of programming Machine/Deep Learning algorithms and optimization using the Python programming language. The course is structured into five modules:

Module 1 covers the basics of Python programming and the numpy, matplotlib, pandas, and scipy modules. Only basic programming knowledge is required as a prerequisite; no prior experience in Python is necessary, as this module starts from scratch.

In Module 2, students will learn the fundamentals of machine learning techniques, applying them to regressions, classifiers, and clustering. The classes include theoretical explanations and practical application scripts in Python using the scikit-learn library.

Module 3 covers various metaheuristic optimization methods using Python's DEAP module: local search methods based on paths and global search methods based on populations. It also includes a section on reinforcement learning using Python's Gym library.

In Module 4, students will learn the fundamentals of deep learning in Keras and TensorFlow, and how to apply these techniques to solve real-world problems. A wide variety of neural network architectures, such as dense networks, convolutional networks, recurrent networks, and deep reinforcement learning, will be studied from both theoretical and practical perspectives.

Module 5 is dedicated to applications. Over 5 sessions and in self-contained classes, several academic and industry professionals will demonstrate application examples from the previous modules.

The course will be taught by experts in the field of machine learning and optimization, who will share their extensive knowledge and experience with the students, and is aimed at

Students passionate about technology and eager to make the most of the opportunities offered by machine learning and optimization. The skills acquired in this course are highly valued in the industry and can open doors to a wide range of career opportunities.

Student selection criteria based on order of pre-registration. A university degree is not required to access the course.