When we have unclassified and unlabeled data, the system attempts to uncover patterns from the data . This article introduces the basics of machine learning theory, laying down the common concepts and techniques involved. This post is intended for the people starting with machine learning, making it easy to follow the core concepts and get comfortable with machine learning basics. There are too many ensemble techniques to adequately summarize here, but more information can be found in Ref. [23]. Recall that it is necessary to train a supervised algorithm on a training dataset in order to ensure it generalises well… Read More

Coupon Marketing is a strategy used by businesses to lure customers to buy their products. Coupons are an easy and very commonly used strategy that can be used across several domains for discounts and promo codes. This marketing strategy will be the most useful only if it reaches the intended audience. Boston House Prices Dataset consists of prices of houses across different places in Boston. The dataset also consists of information on areas of non-retail business (INDUS), crime rate (CRIM), age of people who own a house (AGE), and several other attributes (the dataset has a total of 14 attributes).… Read More

At Rice CS, we cover multiple subfields of quantum information science and computing including (hybrid/variational) quantum algorithms and quantum characterization/verification. In 2023, Quantum computing is moving out of the basement laboratories of university physics departments and into industrial research and development facilities. The move is backed by the chequebooks of multinational corporations and venture capitalists. In materials science, quantum computers will be able to simulate molecular structures at the atomic scale, making it faster and easier to discover new and interesting materials. This may have significant applications in batteries, pharmaceuticals, fertilisers and other chemistry-based domains. How big quantum computers could keep their qubits under control… Read More

They are used to predict financial series [18, 23], the direction of the stock market [24], macroeconomic variables [25], accounting balance sheet information [26] and a good number of other applications, covering a wide range of areas [27]. A major purpose of this study is to determine, empirically, if their performance exceeds that of statistical methods and how their advantages could be exploited to improve forecasting accuracy. What seems certain is that Chatfield’s prediction of NNs becoming a “breakthrough or passing fad” will not be realized [10]. He notes that the imitation of human thinking is not the sole goal… Read More

The world is growing at an exponential rate and so is the size of the data collected across the globe. The problem has shifted from collecting massive amounts of data to understanding it—turning it into knowledge, conclusions, and actions. Multiple research disciplines, from cognitive sciences to biology, finance, physics, and social sciences, as well as many companies believe that data-driven and “intelligent” solutions are necessary to solve many of their key problems. High-throughput genomic and proteomic experiments can be used to enable personalized medicine. Large data sets of search queries can be used to improve information retrieval. Historical climate data… Read More

Reinforcement machine learning is a machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem. Deep learning and neural networks are credited with accelerating progress in areas such as computer vision, natural language processing, and speech recognition. Frank Rosenblatt creates the first neural network for computers, known as the perceptron. This invention enables computers to reproduce human ways of thinking, forming original ideas on their own. Alan Turing jumpstarts the debate around whether computers possess… Read More