Many problems in machine learning can be expressed by means of a graph with nodes representing training samples and edges representing the relationship between samples in terms of similarity, temporal proximity, or label information. Graphs can in turn be represented by matrices. A special example is the Laplacian matrix, which allows us to assign each node a value that varies only little between strongly connected nodes and more between distant nodes. Such an assignment can be used to extract a useful feature representation, find a good embedding of data in a low dimensional space, or perform clustering on the original samples. In these lecture notes we first introduce the Laplacian matrix and then present a small number of algorithms designed around it.

In recent years, mobile devices have gained increasingly development with stronger computation capability and larger storage. Some of the computation-intensive machine learning and deep learning tasks can now be run on mobile devices. To take advantage of the resources available on mobile devices and preserve users' privacy, the idea of mobile distributed machine learning is proposed. It uses local hardware resources and local data to solve machine learning sub-problems on mobile devices, and only uploads computation results instead of original data to contribute to the optimization of the global model. This architecture can not only relieve computation and storage burden on servers, but also protect the users' sensitive information. Another benefit is the bandwidth reduction, as various kinds of local data can now participate in the training process without being uploaded to the server. In this paper, we provide a comprehensive survey on recent studies of mobile distributed machine learning. We survey a number of widely-used mobile distributed machine learning methods. We also present an in-depth discussion on the challenges and future directions in this area. We believe that this survey can demonstrate a clear overview of mobile distributed machine learning and provide guidelines on applying mobile distributed machine learning to real applications.

Decisions are increasingly taken by both humans and machine learning models. However, machine learning models are currently trained for full automation-they are not aware that some of the decisions may still be taken by humans. In this paper, we take a first step towards making machine learning models aware of the presence of human decision-makers. More specifically, we first introduce the problem of ridge regression under human assistance and show that it is NP-hard. Then, we derive an alternative representation of the corresponding objective function as a difference of nondecreasing submodular functions. Building on this representation, we further show that the objective is nondecreasing and satisfies \xi-submodularity, a recently introduced notion of approximate submodularity. These properties allow simple and efficient greedy algorithm to enjoy approximation guarantees at solving the problem. Experiments on synthetic and real-world data from two important applications-medical diagnoses and content moderation-demonstrate that the greedy algorithm beats several competitive baselines.

In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.

The use of training, validation and test datasets is common but not easily understood. In this post, I attempt to clarify this concept. The post is part of my forthcoming book on learning Artificial Intelligence, Machine Learning and Deep Learning based on high school maths. And then comes up with an important statement: Reference to a "validation dataset" disappears if the practitioner is choosing to tune model hyperparameters using k-fold cross-validation with the training dataset. Model selection: involves selecting optimal parameters or a model.

Plutarch's Lives of the Noble Greeks and Romans, also called Parallel Lives or just Plutarch's Lives, is a series of biographies of famous Ancient Greeks and Romans, from Theseus and Lycurgus to Marcus Antonius. In this article / tutorial -- following the recently published Part 1-- I will continue exploring this book using some of the natural language processing techniques. To help with an easy replication, I adapted the code to Google Colab, and highlighted what is unique to the platform -- otherwise the entire code can be run locally on Python 3.6 . The code is sequentially presented throughout the article and the link to the Github files is embedded at the end as I may skip some minor details or supplementary code. The text used in this analysis has been made available by Project Gutenberg.

You're looking for a complete Support Vector Machines course that teaches you everything you need to create a SVM model in R, right? You've found the right Support Vector Machines techniques course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines.

Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition associated with significant healthcare costs; early diagnosis could substantially reduce these. The economic impact of autism reveals an urgent need for the development of easily implemented and effective screening methods. Therefore, time-efficient ASD screening is imperative to help health professionals and to inform individuals whether they should pursue formal clinical diagnosis. Presently, very limited autism datasets associated with screening are available and most of them are genetic in nature. We propose new machine learning framework related to autism screening of adults and adolescents that contain vital features and perform predictive analysis using logistic regression to reveal important information related to autism screening. We also perform an in-depth feature analysis on the two datasets using information gain (IG) and Chi square testing (CHI) to determine the influential features that can be utilized in screening for autism.

"The interests of truth require a diversity of opinions." Banks and lenders are increasingly turning to AI and machine learning to automate their core functions and make more accurate predictions in credit underwriting and fraud detection. ML practitioners can take advantage of a growing number of modeling algorithms, such as simple decision trees, random forests, gradient boosting machines, deep neural networks, and support vector machines. Each method has its strengths and weaknesses, which is why it often makes sense to combine ML algorithms to provide even greater predictive performance than any single ML method could provide on its own. This method of combining algorithms is known as ensembling.

With automation and artificial intelligence expanding across every industry and job function, machine learning – which enables computer systems to learn much like the human brain does – has emerged as one of today's fastest-growing careers. Well-known applications include fraud-detection systems and autonomous cars. To teach the skills to succeed in this rapidly expanding field, Cornell has launched an online certificate program in machine learning. "Machine learning algorithms improve themselves with experience by discovering patterns in data. This approach is extremely powerful but requires a solid understanding of the underlying principles and mechanisms," said Kilian Weinberger, associate professor in Computing and Information Science and faculty author of the certificate program.