Intelligent Analytics2018-10-29T09:46:09+00:00

Project Description

This Call for challenges has expired




Intelligent Analytics

Wayne State University




Intelligent Analytics

Wayne State University

Summary

In this course students will learn computational intelligence methods to solve complex analytics problems and develop effective decision support systems. While the course will address generic topics such as dimensionality reduction, feature selection, clustering, function approximation, pattern recognition, process modelling, forecasting, and optimization, the focus will be on developing advanced analytics solutions. Particular attention will be given to big data problems and thinking. Course will be project centric with the end goal of developing significant solutions to complex problems.

Description

Wayne State University (WSU) is a public research university located in Detroit, Michigan. Founded in 1868, WSU consists of 13 schools and colleges offering nearly 350 programs to more than 27,000 graduate and undergraduate students. Wayne State University is Michigan’s third-largest university and one of the 100 largest universities in the United States.

The WSU main campus encompasses 203 acres (0.82 km2) linking more than 100 education and research buildings in the heart of Detroit. It also has four extension centers in the Metro Detroit providing access to academic courses to students throughout Southeast Michigan. WSU is also an engine in Metro Detroit’s educational, cultural, and economic landscape, through efforts such as its thriving TechTown research and technology center and its partnerships with local hospitals, businesses, law firms, service organizations, and more.

Objectives

At the end of the course, the successful student will be able to develop good understanding for:
1) in-depth understanding for the strengths and weaknesses of different classes of neural networks
2) the recent progress made by the scientific and technical community in the broader field of computational intelligence (including support vector machines, decision trees, Bayesian networks, deep structure learning, and other upcoming and promising nontraditional methods).
3) hands-on experience in the application of computational intelligence methods for developing analytics solutions and decision support systems for significant problems in practice.

Solutions

Students work in teams on a variety of pattern recognition and machine learning applications/challenges. Examples include:
– Production process monitoring for control
– Product demand forecasting
– Manufacturing process modeling for control
– Estimating life-time value of customers for marketing
– Product warranty claims forecasting based on weather and usage patterns
– Estimating surgery duration times based on the nature of the surgery and staff involved
– Predicting in-patient unit admissions in emergency departments
– Evaluating supplier risks

Methods

Variety of Artificial Neural Networks for machine learning, including deep learning. Students will rely on MATLAB computing environment (in particular, Neural Network and Statistics & Machine Learning ToolBoxes) as well as Hadoop/Spark computing platform (SparkML and SparkR) for large-scale data sets/applications.

Skills

Critical thinking / problem-solvingCuriosityJudgement and Decision Making

Insights

 

DEGREE
Master,PhD
WORK EXPERIENCES
0 years
STUDENT AGE
25-34 years old
COUNTRY OF ORIGIN
UNITED STATES

Details

Challenge submission deadline:
29 December 2017

Solving the challenges start/end:
08 January 2018 – 23 April 2018

Course duration:
105 days

No. of challenges called:
1 challenge

No. of students:
25 students

Team size:
5 students / team

Solution output language:
English

Summary

In this course students will learn computational intelligence methods to solve complex analytics problems and develop effective decision support systems. While the course will address generic topics such as dimensionality reduction, feature selection, clustering, function approximation, pattern recognition, process modelling, forecasting, and optimization, the focus will be on developing advanced analytics solutions. Particular attention will be given to big data problems and thinking. Course will be project centric with the end goal of developing significant solutions to complex problems.

Description

Wayne State University (WSU) is a public research university located in Detroit, Michigan. Founded in 1868, WSU consists of 13 schools and colleges offering nearly 350 programs to more than 27,000 graduate and undergraduate students. Wayne State University is Michigan’s third-largest university and one of the 100 largest universities in the United States.

The WSU main campus encompasses 203 acres (0.82 km2) linking more than 100 education and research buildings in the heart of Detroit. It also has four extension centers in the Metro Detroit providing access to academic courses to students throughout Southeast Michigan. WSU is also an engine in Metro Detroit’s educational, cultural, and economic landscape, through efforts such as its thriving TechTown research and technology center and its partnerships with local hospitals, businesses, law firms, service organizations, and more.

Objectives

At the end of the course, the successful student will be able to develop good understanding for:
1) in-depth understanding for the strengths and weaknesses of different classes of neural networks
2) the recent progress made by the scientific and technical community in the broader field of computational intelligence (including support vector machines, decision trees, Bayesian networks, deep structure learning, and other upcoming and promising nontraditional methods).
3) hands-on experience in the application of computational intelligence methods for developing analytics solutions and decision support systems for significant problems in practice.

Solutions

Students work in teams on a variety of pattern recognition and machine learning applications/challenges. Examples include:
– Production process monitoring for control
– Product demand forecasting
– Manufacturing process modeling for control
– Estimating life-time value of customers for marketing
– Product warranty claims forecasting based on weather and usage patterns
– Estimating surgery duration times based on the nature of the surgery and staff involved
– Predicting in-patient unit admissions in emergency departments
– Evaluating supplier risks

Methods

Variety of Artificial Neural Networks for machine learning, including deep learning. Students will rely on MATLAB computing environment (in particular, Neural Network and Statistics & Machine Learning ToolBoxes) as well as Hadoop/Spark computing platform (SparkML and SparkR) for large-scale data sets/applications.

Skills

Critical thinking / problem-solvingCuriosityJudgement and Decision Making

Insights

 

Details

Challenge submission deadline:
29 December 2017

Solving the challenges start/end:
08 January 2018 – 23 April 2018

Course duration:
105

No. of challenges called:
1 challenge challenge

No. of students:
25

Team size:
5 students / team

Solution output language:
English

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This student learning experience is…

Wayne State University (WSU) is a public research university located in Detroit, Michigan. Founded in 1868, WSU consists of 13 schools and colleges offering nearly 350 programs to more than 27,000 graduate and undergraduate students. Wayne State University is Michigan’s third-largest university and one of the 100 largest universities in the United States.

The WSU main campus encompasses 203 acres (0.82 km2) linking more than 100 education and research buildings in the heart of Detroit. It also has four extension centers in the Metro Detroit providing access to academic courses to students throughout Southeast Michigan. WSU is also an engine in Metro Detroit’s educational, cultural, and economic landscape, …

30.001-50.000
students

UNITED STATES
Detroit

This student learning experience is…

About Wayne State University

Wayne State University (WSU) is a public research university located in Detroit, Michigan. Founded in 1868, WSU consists of 13 schools and colleges offering nearly 350 programs to more than 27,000 graduate and undergraduate students. Wayne State University is Michigan’s third-largest university and one of the 100 largest universities in the United States.

The WSU main campus encompasses 203 acres (0.82 km2) linking more than 100 education and research buildings in the heart of Detroit. It also has four extension centers in the Metro Detroit providing access to academic courses to students throughout Southeast Michigan. WSU is also an engine in Metro Detroit’s educational, cultural, and economic landscape, …

30.001-50.000
students

UNITED STATES
Detroit

Challenge Submission

Although your prime goal may be to source “innovative” solutions in form of reports, analysis, prototypes and MVPs, undoubtedly you will get in contact with a very diverse group of student and faculty talents from all over the world.

Instantly, you will become and serve as a partner in promoting solutions by offering invaluable rapid feedback to students that collaborate with you as problem solvers based on your real-world experience and deep expertise.

To be more specific, as a digital challenge sponsor you bring context, real insights and data of a particular area to the table. You clarify, frame, and pitch your challenges prior to exploring solutions with your student teams.

Your collaboration efforts, will help student teams become thought after problem solvers with applied skill sets that will lead to better solutions overall.

As a challenge sponsor you gain access to the platform and respective call for challenges by subscribing to TELANTO´s Academic Business Network.

Suggestions

Interested challenge sponsors should be:

  • Subject matter experts in the field they are applying for, assuring a smooth collaboration with the responsible faculty and student teams.
  • Committed to a collaboration over a period of 12 weeks.
  • Available to spend approx. 10h in total to collaborate with the student teams.

Next Steps

Challenge applicants will be presented to the responsible faculty member for challenge review and pre-selection. If your proposal fits the course syllabus you will be invited to discuss your challenge during a 30 minutes scoping conversation with the professor.

Collaboration Process

Ready to start?

Challenge submission deadline:
29 December 2017

Solving the challenges start/end:
08 January 2018 – 23 April 2018

Solution output:
Report

Frequently Asked Questions

What about confidentiality?

Your information around challenges, collaboration and outputs is kept strictly confidential. While we use your company information and most likely logo and/or trademarks to present you to the academic partners, no third-party, which is not directly involved in the collaboration will never be granted access to sensitive information. As part of the Terms of use, all students agree to the confidentiality clause, when they log in to the network for the first time. Some of our clients choose to address confidentiality in a separate document.

I need to have certainty about any Intellectual and Industrial Rights originating from a collaboration!

The standard Terms & Conditions as well as the Terms of Use foresee, that students cede their IP-rights in favour of the company, when the company contributes to the resolution of the challenges, in terms of providing non-publicly available information, research, analysis, etc. and contribute otherwise with internal resources. A full reading of the Terms & Conditions is made available here: https://telanto.com/telanto-terms-conditions/.

How does the engagement model and collaboration work?

Our role-based platform structures and standardises the process in a simple and intuitive manner with a number of key interaction points, such as the kick-off, mid-term touch points and final presentation. The figure below illustrates the process with the three key roles, being the Challenge Sponsor (CS), Professor and the Student.

Is there any academic supervision of the students working on my challenges?

All collaborations promoted on the Academic Business Network are part of curricular or academically supervised extracurricular activities. For the greater part of the collaborations the challenges are worked on as part of the academic program of a specific course in a bachelor, master or MBA degree, so students performance is in direct correlation to the achievement of course credits and grades.

What is the typical collaboration period with teams of students?

Average collaboration periods take 75 days from kick-off to final presentation. New intense formats with shorter periods are currently experimented with to understand the quality and validity of potential solution outputs.

How much of my time is required to make a collaboration successful?

Half an hour every five to ten days are the standard reports we get from our customers in the higher satisfaction percentiles. Most of the time is required in the preparation, alignment and initial collaboration phase of the challenges.

Do I need to be at the university at some point in time during the engagement?

Half an hour every five to ten days are the standard reports we get from our customers in the higher satisfaction percentiles. Most of the time is required in the preparation, alignment and initial collaboration phase of the challenges.

What is the unique value proposition of TELANTO compared to its competitors?

TELANTO is the sole service provider of a truly global network of universities and companies for university-industry collaboration. With its proprietary technology TELANTO provides novel processes for academia to manage industry relations and collaborations with ease and equally does so for companies with a need for more innovation and problem solving power in search for new ways to detect smart talents.

My company has already University Alliances, do I need TELANTO?

Traditional university alliances are focussed on research and/or short term hiring activities. While those have their certain rationale, TELANTO creates an unprecedented transparency for collaboration possibilities in a broad range of areas, degrees and geographies at any given time. Professionals, managers and leaders within organisations get the possibility to tap into teams of students around the world to start tackle their challenges within much shorter timeframes and practically without bureaucratic effort, than with traditional models.

What type of outcomes can I expect from students?

Most frequent outcomes of collaborations are:

  • Concepts, Analysis and qualitative research
  • Experimental results and scenario projections
  • Prototypes, clickable mockups, MVPs

All in the context of a given challenge, field of study and respective degree as well as experience of the students.

As part of every Call for Challenges from professors, you will be able to consult past and/or expected outcomes for the challenge sponsor to understand the fit of the collaboration opportunity to your challenges at hand.

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