We introduce and analyze an extension to the matching problem on a weighted bipartite graph: Assignment with Type Constraints. The two parts of the graph are partitioned into subsets called types and blocks; we seek a matching with the largest sum of weights under the constraint that there is a pre-specified cap on the number of vertices matched in every type-block pair. Our primary motivation stems from the public housing program of Singapore, accounting for over 70\% of its residential real estate. To promote ethnic diversity within its housing projects, Singapore imposes ethnicity quotas: each new housing development comprises blocks of flats and each ethnicity-based group in the population must not own more than a certain percentage of flats in a block. Other domains using similar hard capacity constraints include matching prospective students to schools or medical residents to hospitals. Limiting agents' choices for ensuring diversity in this manner naturally entails some welfare loss. One of our goals is to study the trade-off between diversity and social welfare in such settings. We first show that, while the classic assignment program is polynomial-time computable, adding diversity constraints makes it computationally intractable; however, we identify a $\tfrac{1}{2}$-approximation algorithm, as well as reasonable assumptions on the weights that permit poly-time algorithms. Next, we provide two upper bounds on the {\em price of diversity} -- a measure of the loss in welfare incurred by imposing diversity constraints -- as functions of natural problem parameters. We conclude the paper with simulations based on publicly available data from two diversity-constrained allocation problems -- Singapore Public Housing and Chicago School Choice -- which shed light on how the constrained maximization as well as lottery-based variants perform in practice.

Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years. The simulation environment in which the agents interact is an essential component in any reinforcement learning problem. The environment simulates the dynamics of the agents' world and hence provides feedback to their actions in terms of state observations and external rewards. To ease the design and simulation of such environments this work introduces APES, a highly customizable and open source package in Python to create 2D grid-world environments for reinforcement learning problems. APES equips agents with algorithms to simulate any field of vision, it allows the creation and positioning of items and rewards according to user-defined rules, and supports the interaction of multiple agents.

Esri's continued advancements in data storage and parallel and distributed computing make solving problems at the intersection of machine learning (ML) and GIS increasingly possible. ML refers to a set of data-driven algorithms and techniques that automate the prediction, classification, and clustering of data. ML can be computationally intensive and often involves large and complex data. It can play a critical role in spatial problem-solving in a wide range of application areas from multivariate prediction to image classification to spatial pattern detection. Based on the analysis of seven years of traffic accident data, the model predicted areas with the highest risk for accidents.

On a balmy summer's day in San Francisco, Milena Marinova is sitting on the roof terrace of the offices of Pearson, a company in the midst of a radical transformation from publishing powerhouse to digital-education platform, wrapped in a gray shawl and explaining how she plans to build advanced, deep-learning algorithms that could educate the next generation of students. This is no easy task. With millions of students using its education-software, Pearson has amassed "terrabytes" of data from student homework and even textbooks that have been digitized, data that Marinova is now pulling together to build software that can automatically give students feedback on their work like a teacher would. Instead of just telling them that an answer is right or wrong, a future update to Pearson's math homework tool will give more detailed feedback on how they went wrong in the steps taken to get an answer, Marinova told Forbes in an interview. Pearson is starting with math because the topic is relatively easy to structure and digitize.

Nowadays we hear very often the words "Recommender systems" and mainly it's because they are quite often used by companies for different purposes, such as to increase sales (items' suggestion while purchasing Amazon: user that have bought this as also bought this) or in suggestions to customers to give them a better customer experience (film suggestion Netflix) or also in advertising to target the right people based on preferences similarities. The recommender systems are basically systems that can recommend things to people based on what everybody else did. Here there is an example of film suggestion taken from an online course. I want to thank Frank Kane for this very useful course on Data Science and Machine Learning with Python. Here there is the course's link in case you would like to go deeper with Data Science.

"In two years, China will be ahead of the United States in AI (artificial intelligence)," states Denis Barrier, CEO of global venture firm Cathay Innovation. If so, China will largely determine how this technology transforms the world. Today's contest is more than a race for dominance in a new technology -- it's one between authoritarianism and democracy. "AI is the world's next big inflection point," says Ajeet Singh, CEO of ThoughtSpot in Palo Alto. Artificial intelligence is machine learning, which self-learns programmed tasks, using data, and the more it gets, the more learned it becomes.

When Argho Chatterjee decided to pursue UpGrad and IIIT Bangalore's PG Program in Machine Learning and Artificial Intelligence, he knew he was diving straight into coding his own artificial neural networks, and had a fair idea that this technology could help him solve real-world problems. What came as a pleasant surprise was that he had the access to a personalised learning environment provided by the prestigious institute through its partnership with distinguished online education venture – UpGrad. The two institutes have been working seamlessly to provide learners with an advanced curriculum, projects created in collaboration with the industry experts, and tailor-made support for AI career choices. In fact, the acclaimed degree went on to help Argho make a transition to the role of a Data Scientist ( Deep Learning (AI)) at Samsung R&D with 80% CTC hike! Learning in a personalised environment under great faculty, Argho brushed up on the basics, imbibed conceptual knowledge, and acquired full-fledged knowledge of the field.

Stochastic momentum methods have been widely adopted in training deep neural networks. However, their theoretical analysis of convergence of the training objective and the generalization error for prediction is still under-explored. This paper aims to bridge the gap between practice and theory by analyzing the stochastic gradient (SG) method, and the stochastic momentum methods including two famous variants, i.e., the stochastic heavy-ball (SHB) method and the stochastic variant of Nesterov's accelerated gradient (SNAG) method. We propose a framework that unifies the three variants. We then derive the convergence rates of the norm of gradient for the non-convex optimization problem, and analyze the generalization performance through the uniform stability approach. Particularly, the convergence analysis of the training objective exhibits that SHB and SNAG have no advantage over SG. However, the stability analysis shows that the momentum term can improve the stability of the learned model and hence improve the generalization performance. These theoretical insights verify the common wisdom and are also corroborated by our empirical analysis on deep learning.

Level assessment for foreign language students is necessary for putting them in the right level group, furthermore, interviewing students is a very time-consuming task, so we propose to automate the evaluation of speaker fluency level by implementing machine learning techniques. This work presents an audio processing system capable of classifying the level of fluency of non-native English speakers using five different machine learning models. As a first step, we have built our own dataset, which consists of labeled audio conversations in English between people ranging in different fluency domains/classes (low, intermediate, high). We segment the audio conversations into 5s non-overlapped audio clips to perform feature extraction on them. We start by extracting Mel cepstral coefficients from the audios, selecting 20 coefficients is an appropriate quantity for our data. We thereafter extracted zero-crossing rate, root mean square energy and spectral flux features, proving that this improves model performance. Out of a total of 1424 audio segments, with 70% training data and 30% test data, one of our trained models (support vector machine) achieved a classification accuracy of 94.39%, whereas the other four models passed an 89% classification accuracy threshold.

Amongst the analysts like Gartner, Forrester and IDC, Artificial Intelligence dominates their predictions for the future. You may well ask, 'what is artificial intelligence?'. The answer to this question is as under: "Artificial Intelligence is when a machine starts behaving like a human being" Technologically, machines do not have any intelligence and they just obey the commands given by their masters. However, when machines start displaying natural intelligence, then that is called artificial intelligence. In technical words, 'any machine that judges its environment and takes action to optimize its chance of achieving its goals.