Machine learning is a set of techniques that allow machines to learn from data and experience, rather than requiring humans to specify the desired behavior by hand. Over the past two decades, machine learning techniques have become increasingly central both in AI as an academic field, and in the technology industry. This course provides a broad introduction to some of the most commonly used ML algorithms. It also serves to introduce key algorithmic principles which will serve as a foundation for more advanced courses, such as CSC412/2506 (Probabilistic Learning and Reasoning) and CSC421/2516 (Neural Networks and Deep Learning).

A company that operates facial recognition systems in China has exposed the personal information of 2.5 million people after leaving a database unprotected. Facial recognition system showing a blue interface with a human head and biometrics data, with a grid of relevant points connected to facial features: used for survellaince, privacy control and identity tracking (Big Brother). A company that operates facial recognition systems in China has exposed the personal information of 2.5 million people after leaving a database unprotected, it has emerged. It was discovered by Dutch cybersecurity researcher Victor Gevers, who works for the GDI Foundation, a non-profit dedicated to reporting security issues. He tweeted: "There is this company in China named SenseNets. They make artificial intelligence-based security software systems for face recognition, crowd analysis, and personal verification. And their business IP and millions of records of people tracking data is fully accessible to anyone."

A team of researchers from Bioversity International in Africa has created a smartphone app to help banana farmers protect their crops against diseases and pests. The Tumaini App (meaning'hope' in Swahili) is based on artificial intelligence algorithms that have been trained to recognize five major diseases and one common pest affecting the world's favorite fruit, demonstrating accuracy of more than 90 per cent in most models. The software has been tested in Colombia, the Democratic Republic of the Congo, India, Benin, China, and Uganda. Tumaini can recommend the means of addressing a specific disease and automatically upload identification data into a global database to help coordinate international response. It is hoped that the app can stop disease outbreaks and protect the livelihood of small, independent farmers.

SqwidNet, in partnership with Sigfox, has concluded the second round of its Internet of Things (IoT) SA University Challenge with ten university teams competing in the final pitch presentation day this week. The programme is designed to challenge students to develop and create innovative projects focused on building solutions that support the UN Sustainable Development Goals using SqwidNet / Sigfox technology. "We were astounded by the creative thinking displayed by the ten teams that presented their solutions to the judges this week," says Phathizwe Malinga, managing director of SqwidNet. "The solutions presented ranged from agricultural solutions for early pest detection to avoid crop losses, to generating electricity from plants by collecting electrons from roots in an anode and converting that into electricity. We also saw an IoT water monitoring solution, an early fire detection for rural communities and a two-way learning solution using artificial intelligence."

Forecasting the performance of a deep neural network is a nightmare for every data scientist. Every month, dozens of new deep learning research algorithms are published making incredible claims about their performance. However, applying those algorithms to real world problems requires a leap of faith that the model can achieve similar levels of performance with unseen datasets. Not surprisingly, many of the research algorithms that performed incredibly well for specific datasets miserably fail when apply to different domains as a clear manifestation of the famous "No Free Lunch Theorem". Very recently, researchers from IBM's artificial intelligence(AI) lab in Zurich published a new paper proposing a method that uses neural networks to predict the performance of a new model prior to training.

One of the most spectacular facts of the last two centuries of economic history is the exponential growth in GDP per capita in most of the world. Figure 1 shows the rise (and the difference) in living standards for five countries since 1000 AD. This economic progress, unprecedented in human history, would be impossible without major breakthroughs in technology. The economic historian Joel Mokyr has argued that the Enlightenment in Britain brought new ways to transfer scientific discoveries into practical tools for engineers and artisans. The steam engine, electricity, sanitation are examples of technological discoveries that propelled the engine of economic growth, increasing standards of living across the planet.

And according to a British company that is halfway to success. Literally, as Winnow states their innovation, which is based on analysing the food thrown away, can reduce the costly and environmentally-damaging food waste by 50 percent. According to the United Nations (UN), one-third of all food produced in the world - approximately 1.3 billion tonnes - is lost or wasted every year. Food loss and waste generate about eight percent of global greenhouse gas emissions. Using smart technology can be part of the solution.

Superhuman performance by artificial intelligence (AI) has been demonstrated in two-player, deterministic, zero-sum, perfect-information games (1) such as chess, checkers (2), Hex, and Go (3). Research using AI has broadened to include games with challenging attributes such as randomness, multiple players, or imperfect information. Randomness is a feature of dice games, and card games include the additional complexity that each player sees some cards that are hidden from others. These aspects more closely resemble real-world situations, and this research may thus lead to algorithms with wider applicability. On page 885 of this issue, Brown and Sandholm (4) show that a new computer player called Pluribus exceeds human performance for six-player Texas hold'em poker.

The human recruiting process has a long timeframe and high complexity, but can simplified into four key words: searching, screening, interviewing and hiring. Today's large global labour force and convenient online application systems however have put a stain on the screening part of the recruiting process. An average of 250 resumes are received for each corporate job opening -- a number that continues increasing -- and it takes an experienced recruiter about five minutes to review each resume. Moreover, in spite of the time-consuming resume reviewing process, hiring remains plagued by randomness and uncertainty often due to unconscious human subjectivity and biases. Many companies around the world are asking the same question: How to objectively and efficiently identify the best candidates from a huge pile of resumes?