"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
The dataset contains information about credit applicants. Banks, globally, use this kind of dataset and type of informative data to create models to help in deciding on who to accept/refuse for a loan. After all the exploratory data analysis, cleansing and dealing with all the anomalies we might (will) find along the way, the patterns of a good/bad applicant will be exposed to be learned by machine learning models. The goal is to train the best machine learning model to maximize the predictive capability of deeply understanding the past customer's profile minimizing the risk of future loan defaults. The metric used for the models' evaluation is the ROC AUC given that we're dealing with a highly unbalanced data.
Hierarchical Clustering uses the distance based approach between the neighbor datapoints for clustering. Each data point is linked to its nearest neighbors. There are two ways you can do Hierarchical clustering Agglomerative that is bottom-up approach clustering and Divisive uses top-down approaches for clustering. In this tutorial, I will use the popular approach Agglomerative way. In order to find the number of subgroups in the dataset, you use dendrogram. It allows you to see linkages, relatedness using the tree graph. You will find many use cases for this type of clustering and some of them are DNA sequencing, Sentiment Analysis, Tracking Virus Diseases e.t.c. Popular Use Cases are Hospital Resource Management, Business Process Management, and Social Network Analysis. Here we are importing dendrogram, linkage, cluster, and cophenet from the scipy.cluster.hierarchy
Principal Component Analysis (PCA) is a Machine Learning algorithm used for various applications such as dimensionality reduction, data/image compression, feature extraction, and so on. The most common usage of PCA is dimensionality reduction (and we will see that in action below). PCA is basically used to extract/find patterns in a given dataset.
AI, computer vision and machine learning systems proved that machines are better and faster than humans analyzing big data. Today, organizations have large datasets of patient data and insights about diseases through techniques like Genome Wide Association Studies (GWAS). Using AI, healthcare providers can analyze and interpret the available patient data more precisely for early diagnosis and better treatment. Today, it is possible to say whether a person has the chance to get cancer from a selfie using computer vision and machine learning to detect increased bilirubin levels in a person's sclera, the white part of the eye. As the interest in AI in the healthcare industry continues to grow, there are numerous current AI applications, and more use cases will emerge in the future.
Did you have the chance to attend the 2021 International Conference on Robotics and Automation (ICRA 2021)? Here we bring you the papers that received an award this year in case you missed them. "An essential and challenging use case solved and evaluated convincingly. This work brings to light the artisanal field that can gain a lot in terms of safety and worker's health preservation through the use of collaborative robots. Simulation is used to design advanced control architectures, including virtual walls around the cutting-tool as well as adaptive damping that would account for the operator know-how and level of expertise."
Chief Technology Officer at Integrity Management Services, Inc., where she is leading cutting-edge technology solutions (AI) for clients. Artificial intelligence is ubiquitous today. Most of us do not know where AI is being used and are unaware of the biased decisions that some of these algorithms produce. There are AI tools that claim to infer "criminality" from face images, race from facial expressions and emotion recognition through eye movements. Many of these technologies are increasingly used in applications that impact credit card checks, fraud detection, criminal justice decisions, hiring practices, healthcare outcomes, spreading misinformation, education, lifestyle decisions and more.
The task is incredibly challenging--even expert human lip readers are actually pretty poor at word-for-word interpretation. In 2018, Google subsidiary Deepmind published research unveiling its latest full-sentence lip-reading system. The AI achieved a word error rate (the percent of words it got wrong) of 41 percent on videos containing full sentences. Human lip readers viewing a similar sample of video-only clips had word error rates of 93 percent when given no context about the subject matter and 86 percent when given the video's title, subject category, and several words in the sentence. That study was conducted using a large, custom-curated dataset.
Sanmay Das, Professor, Computer Science, is conducting an exploratory study in the use of techniques from artificial intelligence (AI) to improve early screening and the delivery of targeted assistance to households that are at risk of future homelessness and child maltreatment. Das and the other members of the research team seek to develop novel methods for allocation of scarce housing support to at-risk households, taking into account considerations of both overall efficiency and fairness. This work will necessitate novel problem formulation and algorithm development in AI as well as creating new ethical methods for deciding on how to effectively deliver social services while considering the vast complexity of human behavior. Das is collaborating with Patrick J. Fowler, Associate Professor at Washington University in St. Louis, on this project. The researchers will explore the feasibility of using novel algorithmic techniques to inform societal decision-making on the allocation of scarce resources, with the specific goal of improving service system outcomes for both homelessness and child welfare.
A Feed Forward Neural Network is commonly seen in its simplest form as a single layer perceptron. In this model, a series of inputs enter the layer and are multiplied by the weights. Each value is then added together to get a sum of the weighted input values. If the sum of the values is above a specific threshold, usually set at zero, the value produced is often 1, whereas if the sum falls below the threshold, the output value is -1. The single layer perceptron is an important model of feed forward neural networks and is often used in classification tasks. Furthermore, single layer perceptrons can incorporate aspects of machine learning.