The Department of Computing Science at the University of Alberta invites applications for tenure-track or tenured faculty positions at all levels. The University of Alberta is home to over 31,000 undergraduate students, 7,600 graduate students, and 600 postdoctoral fellows. A successful candidate for the position may be considered as a nominee for, a funded/endowed research chair position, e.g., Canada Research Chair (CRC), in the Faculty of Science, if the appointment advances the strategic considerations of the Department of Computing Science, the Faculty of Science and the University of Alberta. For further information please email the Department Chair's Executive Assistant at firstname.lastname@example.org To assist the University in complying with mandatory reporting requirements of the Immigration and Refugee Protection Act (R203(3)(e), please include the first digit of your Canadian Social Insurance Number in your application (within your cover letter).
In the financial services industry, machine learning is used to analyse vast datasets, for example to identify a customer's credit profile, to identify profitable companies, or to find an optimal investment strategy. Students in the new UCT degree will master machine learning methods and be able to develop their own applications using these methods. Associate Professor and Head of the Department, Francesca Little, outlines the idea behind the degree "We started the MSc in Data Science to give students a thorough understanding of the latest methods in statistical learning. This includes the extremely exciting field of machine learning and artificial intelligence.
Nicholas Fuentes, an 18-year-old student who attended the "Unite the Right" rally in Charlottesville, Va., this past weekend, said that he's received death threats for months over his conservative viewpoints -- enough for him to decide it's time to leave Boston University. "I went to represent this new strain of conservatives, of people in the right wing who are opposed to mass immigration and multiculturalism," Fuentes told Fox News on Thursday. "The picture the media keeps using is of one person with a Nazi flag, there were more one thousand there who didn't have Nazi flags," Fuentes said. "It was one of my first picks after high school," Fuentes continued, adding that the "friendly territory" of the Deep South will enable him to express his opinions freely without jeopardizing his safety.
This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
As you may have gathered, the families of two-class classification, multi-class classification, anomaly detection, and regression are all closely related. Entirely different sets of data science questions belong in the extended algorithm families of unsupervised and reinforcement learning. Another family of unsupervised learning algorithms are called dimensionality reduction techniques. These are called reinforcement learning (RL) algorithms.
For a start, everyone is going to need a much better theoretical understanding of the technologies surrounding computers, communication networks, artificial intelligence and big data. Dynamic analysis of complex situations and the ability to communicate solutions, in presentations or in video form, will be key. The ability to work in a team, constantly adapting to new situations and working patterns, becomes crucial. Partly, this reflects my own preference for biology metaphors for understanding recent changes in the business world.
They're also around in education, where bots increase student engagement or act as teaching assistants. Georgia tech replaced a teaching assistant with a bit that none of the students noticed was a bot – they even put it up for a teaching award. Whether you are answering customer queries about your product or being a teacher answering questions from your students, the same queries and questions keep popping up. As with many areas of AI, we started with ELIZA but have come to the age of algorithms, where companies use bots to deliver services, universities use bots to teach, governments feel the need to ban bots and bots start to rival humans in what they can do in certain domains.
The advanced machine learning system, which goes by the faintly sinister name of Google Vizier, automatically tunes algorithms right across Google's parent company Alphabet. Google Vizier cuts short this tedious manual task by automatically optimising hyperparameters of machine learning models. "Our implementation scales to service the entire hyperparameter tuning workload across Alphabet, which is extensive," they write in a paper released this week, citing an example where Google researchers "used Vizier to perform hyperparameter tuning studies that collectively contained millions of trials for a research project… That research project would not be possible without effective black–box optimisation." As well as helping research, Google Vizier is being put to use at Alphabet, where, its creators write, it "has made notable improvements to production models underlying many Google products, resulting in measurably better user experiences for over a billion people".
The impact of science can continue to grow provided our scientists and science professionals are equipped with skills to create an innovative, sustainable and prosperous future. Specifically, a Future of Jobs report by the World Economic Forum indicates that, by 2020, the skills most sought after by employers will include problem solving, creative thinking, emotional intelligence and interpersonal skills. Leadership education can directly enhance the employability of science graduates, as leadership skills are often the same transferable skills sought by employers. We have recently found that science students choose to enhance their science degree with leadership education specifically to increase their employability and job opportunities outside of science.
This course teaches the basic concepts of computer-aided translation technology, helps students learn to use a variety of computer-aided translation tools, enhances their ability to engage in various kinds of language service in such a technical environment, and helps them understand what the modern language service industry looks like. This course covers introduction to modern language services industry, basic principles and concepts of translation technology, information technology used in the process of language translation, how to use electronic dictionaries, Internet resources and corpus tools, practice of different computer-aided translation tools, translation quality assessment, basic concepts of machine translation, globalization, localization and so on. As a compulsory course for students majoring in Translation and Interpreting, this course is also suitable for students with or without language major background. By learning this course, students can better understand modern language service industry and their work efficiency will be improved for them to better deliver translation service.