The main objective of the course is to provide students with a solid theoretical basis on the design and implementation of machine learning algorithms. Students will learn to design and implement supervised, unsupervised, and reinforcement learning algorithms, and to apply nonlinear optimization principles to improve the performance of machine learning algorithms. Upon completion of the course, students will be able to design and implement customized learning algorithms for specific problems and domains.
Curriculum
teacher profile teaching materials
Theoretical principles and mathematical foundations of Artificial Intelligence
Main Artificial Intelligence algorithms
Supervised neural networks: the multilayer perceptron (MLP) and synaptic weight matrices
Application of an MLP in Matlab and Python for interpolation and approximation (nonlinear regression).
Setting up an external server for low-level programming
Creating an MLP in C/C++ from scratch
Convolutional Neural Networks (CNN): Design in Matlab and Python
Building a CNN in C/C++ from scratch
Recursive Neural Networks (RNN): Design in Matlab and Python
Building an RNN in C/C++ from scratch
Generative Adversarial Neural Networks (GANs): Design in Matlab and Python
Building a GAN in C/C++ from scratch
Unsupervised neural networks
Neural networks with reinforcement learning
https://d2l.ai/
Programme
Introduction to Artificial Intelligence and fields of applicationTheoretical principles and mathematical foundations of Artificial Intelligence
Main Artificial Intelligence algorithms
Supervised neural networks: the multilayer perceptron (MLP) and synaptic weight matrices
Application of an MLP in Matlab and Python for interpolation and approximation (nonlinear regression).
Setting up an external server for low-level programming
Creating an MLP in C/C++ from scratch
Convolutional Neural Networks (CNN): Design in Matlab and Python
Building a CNN in C/C++ from scratch
Recursive Neural Networks (RNN): Design in Matlab and Python
Building an RNN in C/C++ from scratch
Generative Adversarial Neural Networks (GANs): Design in Matlab and Python
Building a GAN in C/C++ from scratch
Unsupervised neural networks
Neural networks with reinforcement learning
Core Documentation
Dive into deep learninghttps://d2l.ai/
Reference Bibliography
Dive into deep learning https://d2l.ai/Attendance
Attendance is optional but highly recommended.Type of evaluation
Design and implementation of a machine learning model teacher profile teaching materials
Theoretical principles and mathematical foundations of Artificial Intelligence
Main Artificial Intelligence algorithms
Supervised neural networks: the multilayer perceptron (MLP) and synaptic weight matrices
Application of an MLP in Matlab and Python for interpolation and approximation (nonlinear regression).
Setting up an external server for low-level programming
Creating an MLP in C/C++ from scratch
Convolutional Neural Networks (CNN): Design in Matlab and Python
Building a CNN in C/C++ from scratch
Recursive Neural Networks (RNN): Design in Matlab and Python
Building an RNN in C/C++ from scratch
Generative Adversarial Neural Networks (GANs): Design in Matlab and Python
Building a GAN in C/C++ from scratch
Unsupervised neural networks
Neural networks with reinforcement learning
https://d2l.ai/
Programme
Introduction to Artificial Intelligence and fields of applicationTheoretical principles and mathematical foundations of Artificial Intelligence
Main Artificial Intelligence algorithms
Supervised neural networks: the multilayer perceptron (MLP) and synaptic weight matrices
Application of an MLP in Matlab and Python for interpolation and approximation (nonlinear regression).
Setting up an external server for low-level programming
Creating an MLP in C/C++ from scratch
Convolutional Neural Networks (CNN): Design in Matlab and Python
Building a CNN in C/C++ from scratch
Recursive Neural Networks (RNN): Design in Matlab and Python
Building an RNN in C/C++ from scratch
Generative Adversarial Neural Networks (GANs): Design in Matlab and Python
Building a GAN in C/C++ from scratch
Unsupervised neural networks
Neural networks with reinforcement learning
Core Documentation
Dive into deep learninghttps://d2l.ai/
Reference Bibliography
Dive into deep learning https://d2l.ai/Attendance
Attendance is optional but highly recommended.Type of evaluation
Design and implementation of a machine learning model