Originally published by Jason Brownlee in 2013, it still is a goldmine for all machine learning professionals. The algorithms are broken down in several categories. Here we provide a high-level summary, a much longer and detailed version can be found here. Below is a much smaller version. I would add HDT, Jackknife regression, density estimation, attribution modeling (to optimize marketing mix), linkage (in fraud detection), indexation (to create taxonomies or for clustering large data sets consisting of text), bucketisation, and time series algorithms.

It seems it may only be a matter of time before the best Go player on the planet is a computer. AlphaGo beat the European champion in Go and was driven by machine learning, a technology that has underpinned the recent major advances in artificial intelligence in computer vision, speech recognition and language translation.1 Machine learning is a data driven approach to artificial intelligence. AlphaGo learnt how to play Go by many games played against itself, and by observing a large history of games played by professional players. The end result is that by the time of its first match against the European Champion AlphaGo had already played many more games of Go than any human could possibly play in their lifetime. And since that win AlphaGo has been actively learning to improve itself. Relentlessly playing all day and all night in an effort to ready itself to play the world champion.

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Two years ago, a small company in London called DeepMind uploaded their pioneering paper "Playing Atari with Deep Reinforcement Learning" to Arxiv. In this paper they demonstrated how a computer learned to play Atari 2600 video games by observing just the screen pixels and receiving a reward when the game score increased. The result was remarkable, because the games and the goals in every game were very different and designed to be challenging for humans. The same model architecture, without any change, was used to learn seven different games, and in three of them the algorithm performed even better than a human! It has been hailed since then as the first step towards general artificial intelligence – an AI that can survive in a variety of environments, instead of being confined to strict realms such as playing chess. No wonder DeepMind was immediately bought by Google and has been on the forefront of deep learning research ever since.

Rosen Blatt, a freshman, joins The Perceptron, a school choir for women, which participates in an a capella competition. The choir girls inject some energy into their repertoire and start to compete with the male rivals. Surprisingly, they discover that the obese girl, called Fat Amy, has the biggest influence on the quality of their singing. The girls master the repertoire through arduous training, and changing their team in order to achieve the best result.

Instead of building smarter computers to bring miraculous solutions, most of these problems are better solved by smarter application of computing. For example, trying to predict the optimal time to trigger a specific stock purchase could be accomplished with a relatively simple model, but imagine a computer trying to determine which stock to purchase. The problem involves a dramatic increase in complexity because the increased number of variables to consider is so much higher. That kind of complexity requires new approaches, and the dynamic nature of the decisions that have to be made doesn't permit months of research to reach an acceptable level of accuracy.

In 1957, Herbert A. Simon, a pioneer in artificial intelligence and later a Nobel Laureate in economics, predicted that in 10 years a computer would surpass humans in what was then regarded as the premier battleground of wits: the game of chess. Though the project took four times as long as he expected, in 1997 my colleagues and I at IBM fielded a computer called Deep Blue that defeated Garry Kasparov, the highest-rated chess player ever. You might have thought that we had finally put the question to rest--but no. Many people argued that we had tailored our methods to solve just this one, narrowly defined problem, and that it could never handle the manifold tasks that serve as better touchstones for human intelligence. These critics pointed to weiqi, an ancient Chinese board game, better known in the West by the Japanese name of Go, whose combinatorial complexity was many orders of magnitude greater than that of chess. Noting that the best Go programs could not even handle the typical novice, they predicted that none would ever trouble the very best players.

Mergers and acquisitions are a reality in the technology world and one that most startups and small businesses have to confront at some point. One of the key questions your organisation will no doubt ask itself if approached for a buy-out or capital injection is: "Am I going to get the best value for my business in the current climate?" Similarly, large enterprises need to consider whether they're getting the most bang for their buck or punch for their pound when mulling an acquisition. So what does the landscape look like in 2016? In the past, the tech world has experienced'boom-and-bust' economic cycles - think the bursting of the dot-com bubble in the late-90s, or the impact of the pan-European recession from as early as 2007.

That was the result of the match between Google's AlphaGo and human champion Lee Sedol at the fiendishly complex game of Go, and it came with a disconcerting question: what next? Where will the machines claim their next victory: putting you out of a job; solving the mysteries of science; bettering human abilities in the bedroom? AlphaGo's success was down to artificial intelligence (AI): the computer program taught itself how to improve its game by playing millions of matches against itself. But the trouble with using games such as chess and Go as measures of technological progress is that they are competitions. There's a winner and there's a loser – and this month's biggest tech news story had a clear victor.

THIS week humankind was delivered a body blow by an artificial intelligence (AI) called AlphaGo that beat Go's world champion, Lee Sedol, so is it now time for humans to let the machines rule the world? Not just yet--while this adds to a growing list of machines that have beaten the best humans at chess, checkers and backgammon, Lee Sedol won a game back against AlphaGo, so there is still hope for us. The ancient Chinese strategy game Go has substantially more moves to consider each turn than chess. With the two players having to look several moves ahead with more possible outcomes than there are atoms in the universe before deciding what move to make. For each move in a game such as Go, the AI uses a tree search that plays out scenarios, notes which lead to the most victories, and then works back to find out the next move that will lead to the best scenario.