The Hitchhiker's Guide to Machine Learning in Python

#artificialintelligence

Machine learning is undoubtedly on the rise, slowly climbing into'buzzword' territory. This is in large part due to misuse and simple misunderstanding of the topics that come with the term. Take a quick glance at the chart below and you'll see this illustrated quite clearly thanks to Google Trends' analysis of interest in the term over the last few years. However, the goal of this article is not to simply reflect on the popularity of machine learning. It is rather to explain and implement relevant machine learning algorithms in a clear and concise way.


Machine Learning - The Hitchhiker's Guide to Python

#artificialintelligence

Machine learning is undoubtedly on the rise, slowly climbing into'buzzword' territory. This is in large part due to misuse and simple misunderstanding of the topics that come with the term. Take a quick glance at the chart below and you'll see this illustrated quite clearly thanks to Google Trends' analysis of interest in the term over the last few years. However, the goal of this article is not to simply reflect on the popularity of machine learning. It is rather to explain and implement relevant machine learning algorithms in a clear and concise way.


Modern Machine Learning Algorithms: Strengths and Weaknesses

#artificialintelligence

In this guide, we'll take a practical, concise tour through modern machine learning algorithms. While other such lists exist, they don't really explain the practical tradeoffs of each algorithm, which we hope to do here. We'll discuss the advantages and disadvantages of each algorithm based on our experience. Categorizing machine learning algorithms is tricky, and there are several reasonable approaches; they can be grouped into generative/discriminative, parametric/non-parametric, supervised/unsupervised, and so on. However, from our experience, this isn't always the most practical way to group algorithms.


The recent years have witnessed a surge of interests in

AAAI Conferences

We first construct an unsupervised discriminative kernel based on discriminant analysis (Fukunaga, 1990), and then use it to derive two specific algorithms, Semi-Supervised Discriminative Regularization (SSDR) and Semi-parametric Discriminative Semi-supervised Classification (SDSC) to realize our strategy.


The Machine Learning Algorithms Used in Self-Driving Cars 7wData

#artificialintelligence

Machine Learning applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors. We examine different algorithms used for self-driving cars. Today, the Machine Learning algorithms are extensively used to find the solutions to various challenges arising in manufacturing self-driving cars. With the incorporation of sensor data processing in an ECU (Electronic Control Unit) in a car, it is essential to enhance the utilization of machine learning to accomplish new tasks. The potential applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors – like lidar, radars, cameras or the IoT (Internet of Things).