Machine Learning
  I: Administrative Information   II: Course Details   III: Topical Outline(s)  Printable Version
Section I: Administrative Information
  Total hours: 42.0
Credit Value: 3.0
Credit Value Notes: N/A
Effective: Winter 2022
Prerequisites: N/A
Corequisites: N/A
Equivalents: INFO70041
Pre/Co/Equiv Notes: INFO70041- Predictive Analytics and Machine Learning

Program(s): Data Engineer, Data Science and Artificial In
Program Coordinator(s): N/A
Course Leader or Contact: N/A
Version: 20220110_00
Status: Approved (APPR)

Section I Notes: Access to course materials and assignments will be available on Sheridan's Learning and Teaching Environment (SLATE). Students will need reliable access to a computer and the internet.

Section II: Course Details

Detailed Description
Students learn to apply various machine learning concepts and techniques to real-word problems. Through the application of python libraries, students create and evaluate supervised and unsupervised Machine Learning models, apply clustering and association rules, and successfully implement predictive models for diverse data types. Learning culminates in an applied project that is reflective of a typical analysis project for those working in the field of data science.

Program Context

Data Engineer Program Coordinator(s): N/A
This course is part of the Data Engineering Micro-Credential; Data Scientist & Artificial Intelligence Micro-Credential.

Data Science and Artificial In Program Coordinator(s): N/A
This course is part of the Data Scientist and Artificial Intelligence micro-credential

Course Critical Performance and Learning Outcomes

  Critical Performance:
By the end of this course, students will have demonstrated the ability to build machine learning models to make predictions on structured and unstructured data.
Learning Outcomes:

To achieve the critical performance, students will have demonstrated the ability to:

  1. Define the concepts of Artificial Intelligence vs. Machine Learning, and explain the branches of topics that use Machine Learning.
  2. Explain the differences between supervised, unsupervised, and reinforcement learning.
  3. Explain and apply clustering and association rules.
  4. Explain the concept of linear regression and interpret your regression analysis output.
  5. Apply Machine Learning concepts to real-world datasets.

Evaluation Plan
Students demonstrate their learning in the following ways:

 Evaluation Plan: ONLINE
 Assignment 1 (Part A)10.0%
 Assignment 1 (Part B)15.0%
 Assignment 2 (Part A)10.0%
 Assignment 2 (Part B)15.0%
 Final project40.0%

Evaluation Notes and Academic Missed Work Procedure:
TEST AND ASSIGNMENT PROTOCOL The following protocol applies to every course offered by Continuing and Professional Studies. 1. Students are responsible for staying abreast of test dates and times, as well as due dates and any special instructions for submitting assignments and projects as supplied to the class by the instructor. 2. Students must write all tests at the specified date and time. Missed tests, in-class/online activities, assignments and presentations are awarded a mark of zero. The penalty for late submission of written assignments is a loss of 10% per day for up to five business days (excluding Sundays and statutory holidays), after which, a grade of zero is assigned. Business days include any day that the college is open for business, whether the student has scheduled classes that day or not. An extension or make-up opportunity may be approved by the instructor at his or her discretion.

Provincial Context
The course meets the following Ministry of Colleges and Universities requirements:


Essential Employability Skills
Essential Employability Skills emphasized in the course:

  • Communication Skills - Respond to written, spoken, or visual messages in a manner that ensures effective communication.
  • Critical Thinking & Problem Solving Skills - Use a variety of thinking skills to anticipate and solve problems.
  • Information Management Skills - Analyze, evaluate, and apply relevant information from a variety of sources.
  • Information Management - Locate, select, organize and document information using appropriate technology and information systems.
  • Personal Skills - Manage the use of time and other resources to complete projects.
  • Personal Skills - Take responsibility for one's own actions, decisions, and consequences.

Prior Learning Assessment and Recognition
PLAR Contact (if course is PLAR-eligible) - Office of the Registrar
Students may apply to receive credit by demonstrating achievement of the course learning outcomes through previous relevant work/life experience, service, self-study and training on the job. This course is eligible for challenge through the following method(s):

  • Challenge Exam
    Notes:  Both a challenge exam and portfolio are required.
  • Portfolio
    Notes:  Both a challenge exam and portfolio are required.

Section III: Topical Outline
Some details of this outline may change as a result of circumstances such as weather cancellations, College and student activities, and class timetabling.
Instruction Mode: Online
Professor: N/A
RequiredOtherNo textbook required.

Applicable student group(s): Continuing and Professional Studies Students
Course Details:

Module 1: Introduction to Machine Learning 

  • AI and Machine Learning
  • Introduction to supervised Machine Learning
  • Introduction to unsupervised Machine Learning
  • Introduction to reinforcement learning

Module 2: Unsupervised Learning

  • Unsupervised Machine Learning concepts
  • Clustering algorithms 
  • Association rules
  • Quiz 1: 10%

Module 3: Supervised Learning

  • Supervised Machine Learning branches
  • Loss and Machine Learning Algorithms
  • Classification Models
  • Regression and Tree Models
  • Decision Trees
  • Random Forest
  • XGBoost

Module 4: Python Programming

  • Python
  • Anaconda
  • Jupyter Notebook
  • Assignment #1: Part A (10%)
  • Assignment #2: Part A (10%)

Module 5: Applied Python

  • Basic Libraries
  • Popular ML Libraries

Module 6: Applied Machine Learning Examples

  • Supervised Machine Learning: Decision Trees in Python
  • Unsupervised Machine Learning: K-Means Clustering in Python
  • Assignment #1: Part B (15%)
  • Assignment #2: Part B (15%)

Module 7: Applied Model Evaluation

  • Underfitting and Overfitting
  • Cross Validation
  • Final Project: 40%


Sheridan Policies

All Sheridan policies can be viewed on the Sheridan policy website.

Academic Integrity: The principle of academic integrity requires that all work submitted for evaluation and course credit be the original, unassisted work of the student. Cheating or plagiarism including borrowing, copying, purchasing or collaborating on work, except for group projects arranged and approved by the professor, or otherwise submitting work that is not the student's own, violates this principle and will not be tolerated. Students who have any questions regarding whether or not specific circumstances involve a breach of academic integrity are advised to review the Academic Integrity Policy and procedure and/or discuss them with the professor.

Copyright: A majority of the course lectures and materials provided in class and posted in SLATE are protected by copyright. Use of these materials must comply with the Acceptable Use Policy, Use of Copyright Protected Work Policy and Student Code of Conduct. Students may use, copy and share these materials for learning and/or research purposes provided that the use complies with fair dealing or an exception in the Copyright Act. Permission from the rights holder would be necessary otherwise. Please note that it is prohibited to reproduce and/or post a work that is not your own on third-party commercial websites including but not limited to Course Hero or OneNote. It is also prohibited to reproduce and/or post a work that is not your own or your own work with the intent to assist others in cheating on third-party commercial websites including but not limited to Course Hero or OneNote.

Intellectual Property: Sheridan's Intellectual Property Policy generally applies such that students own their own work. Please be advised that students working with external research and/or industry collaborators may be asked to sign agreements that waive or modify their IP rights. Please refer to Sheridan's IP Policy and Procedure.

Respectful Behaviour: Sheridan is committed to provide a learning environment that supports academic achievement by respecting the dignity, self-esteem and fair treatment of every person engaged in the learning process. Behaviour which is inconsistent with this principle will not be tolerated. Details of Sheridan's policy on Harassment and Discrimination, Academic Integrity and other academic policies are available on the Sheridan policy website.

Accessible Learning: Accessible Learning coordinates academic accommodations for students with disabilities. For more information or to register, please see the Accessible Learning website (Statement added September 2016)

Course Outline Changes: The information contained in this Course Outline including but not limited to faculty and program information and course description is subject to change without notice. Any changes to course curriculum and/or assessment shall adhere to approved Sheridan protocol. Nothing in this Course Outline should be viewed as a representation, offer and/or warranty. Students are responsible for reading the Important Notice and Disclaimer which applies to Programs and Courses.

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