Pattern Recognition & Machine Learning2018-10-29T09:46:11+00:00

Project Description

This Call for challenges has expired




Pattern Recognition & Machine Learning

Old Dominion University




Pattern Recognition & Machine Learning

Old Dominion University

Summary

Course provides a practical treatment of design, analysis, implementation and applications of algorithms. Topics include multiple learning models: linear models, neural networks, support vector machines, instance-based learning, Bayesian learning, genetic algorithms, ensemble learning, reinforcement learning, unsupervised learning, etc.

Description

Old Dominion University, also known as ODU, is a public, co-educational research university located in Norfolk, Virginia, United States, with two satellite campuses in the Hampton Roads area. It was established in 1930 as the Norfolk Division of the College of William & Mary and is now one of the largest universities in Virginia with an enrollment of 24,670 students for the 2014-2015 academic year. Its campus covers over 251 acres (1.02 km2) straddling the city neighborhoods of Larchmont, Highland Park, and Lambert’s Point, approximately five miles (8.0 km) from Downtown Norfolk.

Old Dominion University is classified as a Carnegie Doctoral, Higher Research Activity University. Old Dominion University provides nearly $2 billion annually to the regional economy. The university offers 168 undergraduate and graduate degree programs to over 24,000 students and is one of the nation’s largest providers of online distance learning courses. Old Dominion University has approximately 124,000 alumni in all 50 states and 67 countries. Old Dominion University derives its name from one of Virginia’s state nicknames, “The Old Dominion”, given to the state by King Charles II of England for remaining loyal to the crown during the English Civil War.

Objectives

Objectives of this course cover the following topics:
1) Introduction
2) Review of mathematics
3) Density estimation
4) Bayesian and Instance-based learning
5) Linear regression
6) Linear classification
7) Multilayer perceptron
8) Sparse learning and deep learning
9) Support vector machine
10) Clustering
11) Ensemble learning and Genetic algorithm

Solutions

Reports

Methods

action-learning / experiential-learning

Skills

Critical thinking / problem-solvingJudgement and Decision Making

Insights

 

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

Details

Challenge submission deadline:
31 December 2017

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

Course duration:
107 days

No. of challenges called:
1 challenge

No. of students:
25 students

Team size:
5 students / team

Solution output language:
English

Summary

Course provides a practical treatment of design, analysis, implementation and applications of algorithms. Topics include multiple learning models: linear models, neural networks, support vector machines, instance-based learning, Bayesian learning, genetic algorithms, ensemble learning, reinforcement learning, unsupervised learning, etc.

Description

Old Dominion University, also known as ODU, is a public, co-educational research university located in Norfolk, Virginia, United States, with two satellite campuses in the Hampton Roads area. It was established in 1930 as the Norfolk Division of the College of William & Mary and is now one of the largest universities in Virginia with an enrollment of 24,670 students for the 2014-2015 academic year. Its campus covers over 251 acres (1.02 km2) straddling the city neighborhoods of Larchmont, Highland Park, and Lambert’s Point, approximately five miles (8.0 km) from Downtown Norfolk.

Old Dominion University is classified as a Carnegie Doctoral, Higher Research Activity University. Old Dominion University provides nearly $2 billion annually to the regional economy. The university offers 168 undergraduate and graduate degree programs to over 24,000 students and is one of the nation’s largest providers of online distance learning courses. Old Dominion University has approximately 124,000 alumni in all 50 states and 67 countries. Old Dominion University derives its name from one of Virginia’s state nicknames, “The Old Dominion”, given to the state by King Charles II of England for remaining loyal to the crown during the English Civil War.

Objectives

Objectives of this course cover the following topics:
1) Introduction
2) Review of mathematics
3) Density estimation
4) Bayesian and Instance-based learning
5) Linear regression
6) Linear classification
7) Multilayer perceptron
8) Sparse learning and deep learning
9) Support vector machine
10) Clustering
11) Ensemble learning and Genetic algorithm

Solutions

Reports

Methods

action-learning / experiential-learning

Skills

Critical thinking / problem-solvingJudgement and Decision Making

Insights

 

Details

Challenge submission deadline:
31 December 2017

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

Course duration:
107

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…

Old Dominion University, also known as ODU, is a public, co-educational research university located in Norfolk, Virginia, United States, with two satellite campuses in the Hampton Roads area. It was established in 1930 as the Norfolk Division of the College of William & Mary and is now one of the largest universities in Virginia with an enrollment of 24,670 students for the 2014-2015 academic year. Its campus covers over 251 acres (1.02 km2) straddling the city neighborhoods of Larchmont, Highland Park, and Lambert’s Point, approximately five miles (8.0 km) from Downtown Norfolk.

Old Dominion University is classified as a Carnegie Doctoral, Higher Research Activity University. Old …

10.001-30.000
students

UNITED STATES
Norfolk

This student learning experience is…

About Old Dominion University

Old Dominion University, also known as ODU, is a public, co-educational research university located in Norfolk, Virginia, United States, with two satellite campuses in the Hampton Roads area. It was established in 1930 as the Norfolk Division of the College of William & Mary and is now one of the largest universities in Virginia with an enrollment of 24,670 students for the 2014-2015 academic year. Its campus covers over 251 acres (1.02 km2) straddling the city neighborhoods of Larchmont, Highland Park, and Lambert’s Point, approximately five miles (8.0 km) from Downtown Norfolk.

Old Dominion University is classified as a Carnegie Doctoral, Higher Research Activity University. Old …

10.001-30.000
students

UNITED STATES
Norfolk

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:
31 December 2017

Solving the challenges start/end:
06 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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