Students who will complete this course can achieve the following course learning outcomes (CLOs):
CLO1: Understanding of fundamental mathematical concepts such as linear algebra, multivariate calculus, probability theory, optimization, and statistics that are essential for machine learning.
CLO2: Ability to apply mathematical concepts to solve machine learning problems and implement machine learning algorithms.
CLO3: Knowledge of linear algebra concepts such as vectors, matrices, eigenvalues and eigenvectors, singular value decomposition, and their applications to machine learning algorithms such as principal component analysis and support vector machines.
CLO4: Understanding of multivariate calculus concepts such as partial derivatives, gradients, optimization techniques, and their applications to deep learning algorithms.
CLO5: Ability to analyse and interpret machine learning models using mathematical techniques.
Linear algebra Probability and statistics and calculus
Mathematics is a fundamental tool for understanding and developing machine learning algorithms. This module provides a comprehensive introduction to the mathematical foundations of machine learning, covering key concepts and techniques that are essential for success in this field. The module begins with a review of linear algebra, including matrix operations, eigenvalues and eigenvectors, and the singular value decomposition. It then covers calculus, including differentiation, integration, optimization, and gradient descent. The module also explores probability theory, including basic concepts such as random variables and probability distributions.
Linear Algebra Operations
Linear Regression
Mean, Variance and Standard Deviation
Mean, Variance and Standard Deviation
Find a matrix or vector norm using NumPy
Find a matrix or vector norm using NumPy
Understanding Hypothesis Testing
Uni-variate Optimization – Data Science
Multi-variate Optimization – Data Science
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 | Fri-12-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
| Session 2 | Mon-15-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 3 | Tue-16-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 4 | Fri-19-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 5 | Mon-22-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 6 | Tue-23-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 7 | Fri-26-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 8 | Mon-29-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 9 | Tue-30-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 10 | Fri-02-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Section I (Previous) | Session 1 | Fri-16-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
| Session 2 | Mon-19-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 3 | Tue-20-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 4 | Fri-23-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 5 | Mon-26-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 6 | Tue-27-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 7 | Fri-01-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 8 | Mon-04-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 9 | Tue-05-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 10 | Fri-08-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Section I (Previous) | Session 1 | Fri-22-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
| Session 2 | Mon-25-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 3 | Tue-26-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 4 | Fri-29-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 5 | Mon-01-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 6 | Tue-02-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 7 | Fri-05-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 8 | Mon-08-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 9 | Tue-09-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 10 | Fri-12-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Section I (Current) | Session 1 | Fri-26-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
| Session 2 | Mon-29-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 3 | Tue-30-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 4 | Fri-03-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 5 | Mon-06-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 6 | Tue-07-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 7 | Fri-10-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 8 | Mon-13-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 9 | Tue-14-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 10 | Fri-17-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Section I (Upcoming) | Session 1 | Fri-31-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
| Session 2 | Mon-03-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 3 | Tue-04-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 4 | Fri-07-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 5 | Mon-10-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 6 | Tue-11-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 7 | Fri-14-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 8 | Mon-17-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 9 | Tue-18-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 10 | Fri-21-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
