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Machine and Deep Learning

Overview

Machine Learning and Natural Language Processing define the current state of the art of Artificial Intelligence. These technologies, which are a form of data mining and data analysis, continuously learn from the provided information. They recognize hidden patterns that often provide dramatic competitive advantages, at relatively low costs to the organization. These technologies create significant improvements in the way we work, interact, and live – producing efficiencies never imagined before. These methods are being applied in a diverse range of industries, including sales, marketing, advertising, health care, criminal justice, finance customer support, cool new industries like self-driving cars, and highly efficient automated homes. Organizations today use these methods not only to improve their core business operations but also for developing new business models.

Deep Learning focuses on those Machine Learning tools that mimic human thought processes. Deep Learning can utilize a wide range of very large data sets (Big Data) in a vast array of formats (unstructured text, speech, images, audio and video). Machine Learning is dependent upon given features of the data to perform classification, detection, or prediction. Deep Learning is not dependent upon the representation of the data. It builds complex concepts from simpler models or data the same way the human brain processes large sets of inherently simpler stimuli to classify, recognize, analyze and synthesize. This layering of the network from simple to complex is Deep Learning. As these networks are inspired by the human brain, they are also called Artificial Neural Networks. Deep Learning algorithms and models are “trained” by the data (with guidance and monitoring from human insight) to solve a particular problem. Deep Learning Systems are designed to self-improve and get better as they process more data again mimicking how the human mind works.

Advances in Machine Learning and Deep Learning are helping to solve a very broad variety of problems including logistics, business process optimization, customer service, and health care. By 2020, IT departments will be monitoring 50 times more data than they are today. This tidal wave of data has driven unprecedented demand for those with the skills required to manage and leverage these very large and diverse data sets into a competitive advantage for their organizations.

According to a recent McKinsey report, one of the key barriers in the adoption of Machine Learning is attracting and retaining the right talent in business people that combine data skills with industry and functional expertise. This program has been designed to help meet these expanding needs of business and industry for professionals who can effectively utilize both Machine and Deep Learning techniques to add value to any business.

Who Should Enroll

This program is intended for professionals in a variety industries and job functions who are looking to help their organization leverage the massive amounts of diverse data they collect and develop self-improving systems that improve their organizations ability to compete in the global market place. Specific job titles that would benefit from this program include: Marketing, Sales, Business Analysts, Data Engineers, Data Analysts, Computer Scientists, Database Administrators, Researchers, Statisticians, and those professionals looking to broaden their skills in this high-demand field while leveraging their unique domain expertise.

Career Insight

Occupational summary for computer and information research scientists. Source: Economic Modeling Specialists Intl.

33,286
Annual Job Openings
(2019)
18.7%
Projected Growth
(2019-2029)
$118k
Median Salary
(Highly experienced workers can earn up to $183k)

Program Benefits

  • Learn from industry experts how to deliver new insights and improve the competitiveness of businesses by applying the art and science of machine and deep learning.
  • Explain what kinds of problems are best suited for machine learning and which are best suited for deep learning
  • Understand and apply machine and deep learning software tools used in industry to solve business problems
  • Explain a variety of learning algorithms and how they are applied to understand differences between unsupervised, semi-supervised, supervised, and reinforcement processes
  • Learn methodologies and tools to apply algorithms using a wide range of real data types including structured and unstructured text, video, and images from internal or external sources (e.g. scraped web data) and evaluate their performance
  • Determine which related software toolkits to consider and how to integrate them into existing data workflows
  • Utilize basic building blocks, general principles, and cloud technologies such as Amazon Web Services (AWS) to design machine learning algorithms
  • Learn the tools and techniques of Natural Language Processing (NLP) and its use in the analysis of human generated content
  • Understand common pitfalls and challenges using neural networks and deep learning tools
  • Understand what hardware or which virtual machines are needed for deep learning
  • Explain the difference between machine and deep learning versus traditional statistical data analysis techniques

Course Schedule

Prerequisite Courses

TitleWinterSpringSummerFall
I&C SCI X426.64
Introduction to Python Programming (2.00 Units)
I&C SCI X426.64
Online to be scheduled to be scheduled Online
I&C SCI X426.59
Intermediate Python (2.00 Units)
I&C SCI X426.59
Online to be scheduled to be scheduled Online
I&C SCI X425.99
Practical Math & Statistics for Data Science (2.50 Units)
I&C SCI X425.99
Online to be scheduled to be scheduled Online

Required Courses

TitleWinterSpringSummerFall
I&C SCI X426.75
Tools and Techniques for Machine Learning (2.00 Units)
I&C SCI X426.75
  to be scheduled   Online
I&C SCI X426.76
Artificial Neural Networks (2.00 Units)
I&C SCI X426.76
Online   to be scheduled  
I&C SCI X426.77
Text Mining and Analytics for Machine Learning (2.00 Units)
I&C SCI X426.77
  to be scheduled    

Elective Courses

TitleWinterSpringSummerFall
Machine and Deep Learning
I&C SCI X425.61
Introduction to Predictive Analytics (2.00 Units)
I&C SCI X425.61
    to be scheduled  
I&C SCI X426.78
Deep Learning Using TensorFlow (2.50 Units)
I&C SCI X426.78
Online   to be scheduled  

Course schedules are subject to change. Individual courses may be taken without enrolling in the full program.

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Academic Calendar

Event Fall 2020 Winter 2021 Spring 2021 Summer 2021
Registration Begins (after 2pm PT) Jul 16 Oct 22 Jan 21 Apr 22
Courses Begin* (week of) Sep 21 Jan 4 Mar 29 Jun 21
Courses End* (week of) Dec 14 Mar 15 Jun 7 Sep 7
Parking Permits Expire Dec 31 Mar 31 Jun 30 Sep 30
Administrative Holidays Nov 11, Nov 26-27, Dec 24-25, Dec 31 Jan 1, Jan 18, Feb 15 Mar 26, May 31 Jul 5, Sep 6

*Actual dates may vary by program.

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Learn How To Earn Your CertificateSpecialized Studies Award Requirements

The Specialized Studies award is provided upon completion of 10 credit units (3 required courses and a minimum of 4 elective credit units) with a grade of “C” or higher in each course. All requirements must be completed within 5 years after the student enrolls in his/her first course. To receive your digital certificate, submit a Request for Certificate with a non-refundable $35 application fee after completing all program requirements. Students not pursuing a certificate are welcome to take individual courses.

On-site Training Available

Our Corporate Training specialists can deliver this program or customize one that fits your organization’s specific needs. Visit Corporate Training or call (949) 824-1847 for information.

English Proficiency Requirement
All certificate programs at UCI Division of Continuing Education (classroom and online formats) require professional-level English language proficiency in listening and note-taking, reading comprehension and vocabulary, written expression, and oral presentation.