This tutorial is part of the Machine learning for developers learning path. In this tutorial, we describe the basics of solving a classification-based machine learning problem, and give you a comparative study of some of the current most popular algorithms. In the open Notebook, click Run to run the cells one at a time. The rest of the tutorial follows the order of the Notebook. Classification is when the feature to be predicted contains categories of values.

FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges. Peter Kairouz and H. Brendan McMahan conceived, coordinated, and edited this work.

In this article, we have explored how we can classify text into different categories using Naive Bayes classifier. We have used the News20 dataset and developed the demo in Python. As the name suggests, classifying texts can be referred as text classification. Usually, we classify them for ease of access and understanding. We don't need human labour to make them sit all day reading texts and labelling categories.

Maximizing the (log) likelihood is equivalent to minimizing the binary cross entropy. There is literally no difference between the two objective functions, so there can be no difference between the resulting model or its characteristics. This of course, can be extended quite simply to the multiclass case using softmax cross-entropy and the so-called multinoulli likelihood, so there is no difference when doing this for multiclass cases as is typical in, say, neural networks. The difference between MLE and cross-entropy is that MLE represents a structured and principled approach to modeling and training, and binary/softmax cross-entropy simply represent special cases of that applied to problems that people typically care about. After that aside on maximum likelihood estimation, let's delve more into the relationship between negative log likelihood and cross entropy.

We have derived theoretical maximum likelihood estimators of trends for standard dynamics and implemented them. We have reframed the problem of trend detection into a classification problem amenable to machine learning methods. We have shown that RNN are in a way a generalization of simple moving average techniques and motivated this by theory. In a simple case, we have shown that this generalization transforms the trend estimation problem into simply locating the state vector into convex polytopes cells. Finally, we have showed empirically that GRU or LSTM cells are on average the best building block to use compared to a broad range of estimators in order to detect trends in time series. Putting the emphasis on learning stylized data and then transferring to real data rather than building complex structures fitted to data is also an important takeaway of this paper. Ongoing preliminary research seems to validate our approach for financial applications. This might pave the way to building efficient market estimators protected against over-fitting.

What AI tools are needed for SE? Ideally, we should have simple rules that peek at data, then say "use this tool" or "use that tool". To find such a rule, we explored 120 different data sets addressing numerous problems, including bad smell detection, predicting Github issue close time, bug report analysis, defect prediction and dozens of other non-SE problems. To this data, we apply a SE-based tool that (a)~out-performs the state-of-the-art for these SE problems yet (b)~fails very badly on standard AI problems. In those results, we can find a simple rule for when to use/avoid the SE-based tool. SE data is often about infrequent issues, like the occasional defect, or the rarely exploited security violation, or the requirement that holds for one special case. But as we show, standard AI tools work best when the target is relatively more frequent. Also, we can exploit these special properties of SE, to great effect (to rapidly find better optimizations for SE tasks via a tactic called "dodging", explained in this paper). More generally, this result says we need a new kind of SE research for developing new AI tools that are more suited to SE problems.

In the past few years the generation of data and our capability to store and process it has grown exponentially. There is a need for scalable analytics frameworks and people with the right skills to get the information needed from this Big Data. Apache Mahout is one of the first and most prominent Big Data machine learning platforms. It implements machine learning algorithms on top of distributed processing platforms such as Hadoop and Spark. Starting with the basics of Mahout and machine learning, you will explore prominent algorithms and their implementation in Mahout development. You will learn about Mahout building blocks, addressing feature extraction, reduction and the curse of dimensionality, delving into classification use cases with the random forest and Naive Bayes classifier and item and user-based recommendation.

Often machine learning methods are applied and results reported in cases where there is little to no information concerning accuracy of the output. Simply because a computer program returns a result does not insure its validity. If decisions are to be made based on such results it is important to have some notion of their veracity. Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods. In situations where inaccuracies are detected boosted contrast trees can often improve performance. A special case, distribution boosting, provides an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.

Continual Learning is a learning paradigm where machine learning mode ls are trained with sequential or streaming tasks. Two notable directions among the recent adva nces in continual learning with neural networks are ( i) variational Bayes based regularization by learning priors from pre vious tasks, and, ( ii) learning the structure of deep networks to adapt to new tasks. S o far, these two approaches have been orthogonal. We present a principled nonparametric Bayesian appr oach for learning the structure of feed-forward neural networks, addressing the shortcomings o f both these approaches. In our model, the number of nodes in each hidden layer can automatically grow with the in troduction of each new task, and inter-task transfer occurs through the overlapping of differ ent sparse subsets of weights learned by different tasks. On benchmark datasets, our model performs comparably or better than the state-of-the-art approaches, while also being able to adaptively infer the evolving network structure in the continual learning setting.

I will discuss a line of work in which we compile common machine learning systems into symbolic representations that have the same input-output behavior to facilitate formal reasoning about these systems. We have targeted Bayesian network classifiers, random forests and some types of neural networks, compiling each into tractable Boolean circuits, including Ordered Binary Decision Diagrams (OBDDs). Once the machine learning system is compiled into a tractable Boolean circuit, reasoning can commence using classical AI and computer science techniques. This includes generating explanations for decisions, quantifying robustness and verifying properties such as monotonicity. I will particularly discuss a new theory for unveiling the reasons behind the decisions made by classifiers, which can detect classifier bias sometimes from the reasons behind unbiased decisions.