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New course on machine learning & quantum computing

posted Dec 4, 2018, 5:42 PM by Jose L. Mendoza-Cortes   [ updated Dec 4, 2018, 6:04 PM ]
2018/12/04: We introduce new courses on machine learning & quantum computing.

More info in page 2 of the Fall 2018 Newsletter of the Scientific Computing Department. Download here.  

Fragments of the article below. 

Mendoza-Cortes introduces new course on machine learning & quantum computing

Jose Mendoza-Cortes is preparing students for the world they will inhabit in the coming twenty-plus years, a
time when an ample population of jobs will disappear, and others – jobs that require more specialized, sophisticated skills – will be the norm. Some who are striving to invent and design this new era are calling it the Fourth Industrial Revolution, a time where technologies bring together physical and virtual worlds, disrupting manufacturing processes, changing the way data is analyzed, and altering how the world consumes goods, and services.

Mendoza-Cortes sees his course as an opportunity to give students a broad foundation for the approaching
changes computing will bring to industry. “At the conclusion of the course, my goal is for students to have a broad
understanding of machine learning, data mining, and statistical pattern recognition, and grasp quantum computing at the first approximation,” Mendoza-Cortes said.

To that end, Mendoza-Cortes has included a wide variety of machine learning and quantum computing topics in the 2-credit hour course. Students are learning the most effective machine learning techniques, the theoretical underpinnings of different machine learning - artificial intelligence, and the practical skills needed to quickly and powerfully apply these techniques to new problems.

“Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.”

In the quantum computing portion of the course, Mendoza-Cortes focuses on topics that pair quantum computing with computation heavy, sophisticated tasks. Quantum generative adversarial learning, for example, uses qubits - the quantum answer to binary’s zeros and ones - to discriminate between real data and false, computer generated data. Other topics the course covers: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels,neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that students learn how to apply learning algorithms to building text understanding (web search, anti-spam), computer vision, medical
informatics, audio, database mining, and other areas. (iv) implementing the first quantum algorithms in a quantum computer simulator.

Students bring their laptops to use during class; out-of-class work requires Python, MATLAB or Jupyter, all of which are provided through departmental computing resources. Machine Learning & Quantum Computing is taught on Friday from 2-3pm in 499 Dirac, the Seminar Room. The course is not intended to be math-heavy at the beginning, and is for anyone interested in machine learning and quantum computing.

The initial course has been successful, and Mendez-Cortes plans to expand the offering to two courses in the coming semester (Spring 2019). The first course, Machine Learning for Science and Engineering, is 3 credit hours. The second is two credit hours and is entitled Introduction to Quantum Computing: Theory and Practice.

For more on these courses, go to: