Course description
In this course, students will learn to apply machine learning concepts to analyze data and make predictions. Students will learn how to collect and wrangle data, to explore data using statistics and visualizations, to transform data for further modeling, to model data using machine learning algorithms to predict data patterns, and to evaluate these model-based predictions. Students will be expected to have prior experience with fundamentals of programming.
Learning activities
Lecture, seminars, demonstrations, and hands-on exercises/projects
Means of assessment
The course evaluation is consistent with the Douglas College Evaluation Policy.
| Labs |
0 - 10% |
| Assignments |
0 - 10% |
| Projects |
15% - 30% |
| Midterm Exam* |
30% - 40% |
| Final Exam* |
30% - 40% |
| Total |
100% |
* Practical hands-on computer exam
In order to pass the course, students must, in addition to receiving an overall course grade of 50%, also achieve a grade of at least 50% on the combined weighted examination components (including quizzes, tests, exams).
Students may conduct research as part of their coursework in this class. Instructors for the course are responsible for ensuring that student research projects comply with College policies on ethical conduct for research involving humans, which can require obtaining Informed Consent from participants and getting the approval of the Douglas College Research Ethics Board prior to conducting the research.
Learning outcomes
At the end of this course, the successful student will be able to:
- Use appropriate tools and libraries needed for data science.
- Explain the concepts of data processing and feature engineering.
- Conduct data wrangling for further machine learning processing.
- Apply statistisc and visulization techniques for data exploration.
- Employ suitable structures to transform data for modeling.
- Utilize various machine learning models and algorithms for different applications.
- Compare different models using appropriate evaluation metrics and strategies.
Textbook materials
Custom courseware, class notes provided by the instructor, and online resources or other textbooks as approved by the department