Students who will complete this course can achieve the following course learning outcomes (CLOs):
CLO1: Understand the basics of unsupervised learning, including clustering, dimensionality reduction, anomaly detection, and association rule mining.
CLO2: Choose appropriate unsupervised learning algorithms based on the characteristics of the dataset and the problem to be solved.
CLO3: Evaluate the performance of unsupervised learning algorithms using metrics such as coherence and consistency of the patterns and relationships discovered by the algorithm.
CLO4: Preprocess and prepare data for unsupervised learning algorithms, including techniques such as feature scaling, normalization, and handling missing data.
Unsupervised Learning, clustering, dimensionality reduction, anomaly detection and hidden patterns or structure
Unsupervised Learning is a type of machine learning in which an algorithm is trained on an unlabeled dataset. The algorithm learns to find patterns and structure in the input data without explicit knowledge of the output. In unsupervised learning, the algorithm is not provided with labeled data, but instead must discover relationships and groupings within the data on its own. The goal of unsupervised learning is to find hidden patterns or structure in the data, such as clusters or associations, that can be used to gain insights or make predictions about new, unseen data. Common examples of unsupervised learning include clustering, dimensionality reduction, and anomaly detection.
The importance of taking NOC courses:
This course is designed to train our students to find jobs in the Canadian labour market using the National Occupational Classification (NOC) and its codes. The Government of Canada developed the NOC to categorize occupational information in the Canadian labour market through a standardized framework and a system that can be easily managed, understood, and unified. Canadian Immigration (i.e., IRCC) uses the NOC to classify jobs and occupations according to specific skill levels. Canada's jobs are ranked according to a person's work and the roles and responsibilities of the job.
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Prof. Saranya is the Instructor
at NSRIC Inc. She is also the Associate Professor in Computer Science
Engineering (ENG) at Anna University Colleges. In addition to her current
affiliation with NSRIC, she holds freelance faculty positions in some other
universities. Prof. Saranya is the founder of Algorithmics Computing Centre in
India, She has mentored projects under the Smart India Hackathon for various ministries.
She has published journals in reputed articles such as Springer, and many
journals indexed by Elsevier. She has also published books in Amazon like
Octave by examples, Points to Ponder for Python, and so on. She has also
published book chapters about the updating of recent trends by IGI global
publishing. In 12 years professional career, Dr, Saranya has Served on academic
or administrative committees to deal with institutional policies along with preparing
and delivering lectures to undergraduate and graduate students on topics such
as programming languages, data structures, networking, software design, AI,
Blockchain technologies and so on.
Prof. Saranya earned a B.Tech
in Information Technology from Anna University, India in 2009, and an M.E in Software
Engineering from Anna University in 2011. Dr. Saranya was awarded PhD in Information
and Communication Engineering in 2019 and an MBA(Information Systems) degree in
2014 by Bharathiyar University, India.
| Section Name | Lecture Name | Lecture Date | Lecture Time (Toronto, Canada - EST Time) |
Lecture Time (Local Time) |
|---|---|---|---|---|
| Section I (Previous) | Session 1 | Tue-16-Jan-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM |
| Session 2 | Fri-19-Jan-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 3 | Mon-22-Jan-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 4 | Tue-23-Jan-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 5 | Fri-26-Jan-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 6 | Mon-29-Jan-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 7 | Tue-30-Jan-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 8 | Fri-02-Feb-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 9 | Mon-05-Feb-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 10 | Tue-06-Feb-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Section I (Previous) | Session 1 | Tue-20-Feb-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM |
| Session 2 | Fri-23-Feb-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 3 | Mon-26-Feb-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 4 | Tue-27-Feb-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 5 | Fri-01-Mar-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 6 | Mon-04-Mar-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 7 | Tue-05-Mar-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 8 | Fri-08-Mar-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 9 | Mon-11-Mar-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 10 | Tue-12-Mar-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Section I (Previous) | Session 1 | Tue-26-Mar-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM |
| Session 2 | Fri-29-Mar-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 3 | Mon-01-Apr-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 4 | Tue-02-Apr-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 5 | Fri-05-Apr-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 6 | Mon-08-Apr-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 7 | Tue-09-Apr-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 8 | Fri-12-Apr-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 9 | Mon-15-Apr-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 10 | Tue-16-Apr-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Section I (Current) | Session 1 | Tue-30-Apr-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM |
| Session 2 | Fri-03-May-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 3 | Mon-06-May-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 4 | Tue-07-May-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 5 | Fri-10-May-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 6 | Mon-13-May-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 7 | Tue-14-May-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 8 | Fri-17-May-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 9 | Mon-20-May-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 10 | Tue-21-May-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Section I (Upcoming) | Session 1 | Tue-04-Jun-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM |
| Session 2 | Fri-07-Jun-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 3 | Mon-10-Jun-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 4 | Tue-11-Jun-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 5 | Fri-14-Jun-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 6 | Mon-17-Jun-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 7 | Tue-18-Jun-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 8 | Fri-21-Jun-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 9 | Mon-24-Jun-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM | |
| Session 10 | Tue-25-Jun-24 | 09:00 AM to 10:00 AM | 07:00 PM to 08:00 PM |
