A Machine Learning Lecture Series Course Bundle, by Sergios Theodoridis

The IEEE Signal Processing Society (SPS) is proud to offer this free course bundle "A Machine Learning Lecture Series" by Prof. Sergios Theodoridis, with course material preparation by Konstantinos Koutroumbas.

The goal of this series of lectures is to introduce the newcomer to the “secrets” of the machine learning (ML) discipline. In the dawn of the 4th industrial revolution era, machine learning is one among the key technologies that drive the advances and the fast evolution of this new historical period.

The series of lectures is intended to cover a major part  of what is considered as basic knowledge in  machine learning. The lectures start from the  definitions of regression and classification and move on from the classics to the most recent advances in the field. The online lectures have been developed to address the needs of those who wish to grasp and understand the basic notions behind the methods and algorithms that have been developed and not just the needs of the  black box type of users of ML algorithms.

The series of lectures comprises five parts.

• Part 1 deals with the basic definitions as well as the fundamentals related to regression and classification.
• Part 2 deals with the classics on classification, starting with the Bayes classifier rule and ending with the classification trees and the "boosting" concept.
• Part 3 presents the notion of kernels and support vector machines.
• Part 4 focuses on deep learning, following a historical development, starting from the classical perceptron and moving on to convolutional neural networks,  recurrent neural networks, adversarial examples and GANs.
• Part 5 presents Bayesian learning, latent variables,  the expectation-maximization algorithm and the variational approximation concept, with applications to Gaussian mixtures and regression.

Part 1, Part 2, and Part 4 are a must.

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A Machine Learning Lecture Series

The IEEE Signal Processing Society (SPS) is proud to offer this free course bundle "A Machine Learning Lecture Series" by Prof. Sergios Theodoridis, with course material preparation by Konstantinos Koutroumbas.

The goal of this series of lectures is to introduce the newcomer to the “secrets” of the machine learning (ML) discipline. In the dawn of the 4th industrial revolution era, machine learning is one among the key technologies that drive the advances and the fast evolution of this new historical period.

The series of lectures is intended to cover a major part  of what is considered as basic knowledge in  machine learning. The lectures start from the  definitions of regression and classification and move on from the classics to the most recent advances in the field. The online lectures have been developed to address the needs of those who wish to grasp and understand the basic notions behind the methods and algorithms that have been developed and not just the needs of the  black box type of users of ML algorithms. 

The series of lectures comprises five parts. 

• Part 1 deals with the basic definitions as well as the fundamentals related to regression and classification. 
• Part 2 deals with the classics on classification, starting with the Bayes classifier rule and ending with the classification trees and the "boosting" concept.
• Part 3 presents the notion of kernels and support vector machines.
• Part 4 focuses on deep learning, following a historical development, starting from the classical perceptron and moving on to convolutional neural networks,  recurrent neural networks, adversarial examples and GANs.
• Part 5 presents Bayesian learning, latent variables,  the expectation-maximization algorithm and the variational approximation concept, with applications to Gaussian mixtures and regression.

Part 1, Part 2, and Part 4 are a must.