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Machine Learning

SCS-3253
Closed
University of Toronto
University of Toronto
Toronto, Ontario, Canada
Saeid Abolfazli
Instructor
(1)
2
Timeline
  • March 10, 2020
    Experience start
  • March 13, 2020
    Project Scope Meeting
  • March 14, 2020
    2nd Touch Point
  • April 4, 2020
    Project Presentation
  • April 14, 2020
    Experience end
Experience
6/10 project matches
Dates set by experience
Preferred companies
Anywhere
Any
Any industries
Categories
Communications Operations Project management Marketing strategy
Skills
business analytics business consulting data analysis machine learning artificial intelligence
Learner goals and capabilities

Throughout the course students under instructor supervision will create machine learning solutions to enable your organization to predict future events of interest.

Learners
Any level
60 learners
Project
30 hours per learner
Learners self-assign
Teams of 4
Expected outcomes and deliverables

The final project deliverables will include:

  1. The POC software created to solve the machine learning problem - it is not a production ready solution
  2. Recommendation report on how to use the model
  3. A final presentation
Project timeline
  • March 10, 2020
    Experience start
  • March 13, 2020
    Project Scope Meeting
  • March 14, 2020
    2nd Touch Point
  • April 4, 2020
    Project Presentation
  • April 14, 2020
    Experience end
Project Examples

Machine learning is a field in computer science that uses statistical techniques to give computer systems the ability to learn from data without being explicitly programmed. Developing effective machine learning is often challenging; finding patterns is difficult and there is often not enough training data available.

Project examples include, but are not limited to:

  • Predicting future events of interest: For instance, in marketing, machine learning enables businesses to predict customer intents, including purchasing a product or terminating their service contract. In area of predictive maintenance, machine learning can predict health status of different equipments and help business take proactive action towards maintaining operating at low cost.

  • Spotting anomalies in data sets: Detecting fraud is a use case in point of sales and banking transactions
Companies must answer the following questions to submit a match request to this experience:

Ensure you have "enough real data" and be ready to share data with the instructor 1-2 week before the project starts. This is crucial in success of the project.

Be available for a quick phone call with the instructor to initiate your relationship and confirm your scope is an appropriate fit for the course.

Provide a dedicated contact who is available to answer periodic emails or phone calls over the duration of the project to address students' questions.

Provide an opportunity to students to present their work and receive feedback

Be available for at least 2 follow up meetings (phone call, preferably in-person) with the students to monitor the progress, clarify doubts, and answer questions.