img

Mathematics for Machine Learning

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 Summary

NOC Number:

Dr. Saranya Vasanthamani | Author Level 1

What You Will Learn

Course Learning Outcome (CLO):

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.

Keywords:

Linear algebra Probability and statistics and calculus

Course Description:

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.

2 Natural and applied sciences and related occupations
21 Professional occupations in natural and applied sciences
212 Professional occupations in applied sciences (except engineering)
2122 Computer and information systems professionals

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.

0.0

0 Reviews

5
0
4
0
3
0
2
0
1
0
Meet Your Instructor

Instructor
0.0 Rating
0 Students
Author Level 1
16 Courses
About Instructor

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 (Previous) 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 (Previous) 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
Section I (Current) Session 1 Fri-05-Jul-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 2 Mon-08-Jul-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 3 Tue-09-Jul-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 4 Fri-12-Jul-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 5 Mon-15-Jul-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 6 Tue-16-Jul-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 7 Fri-19-Jul-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 8 Mon-22-Jul-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 9 Tue-23-Jul-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 10 Fri-26-Jul-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Section I (Upcoming) Session 1 Fri-09-Aug-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 2 Mon-12-Aug-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 3 Tue-13-Aug-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 4 Fri-16-Aug-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 5 Mon-19-Aug-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 6 Tue-20-Aug-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 7 Fri-23-Aug-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 8 Mon-26-Aug-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 9 Tue-27-Aug-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
Session 10 Fri-30-Aug-24 08:00 PM to 09:00 PM 06:00 AM to 07:00 AM
video
  • Course Duration
    13 Hours 0 Minutes
  • Course Level
    Foundation
  • Discipline
    Information and Communication Technology (ICT)
  • Language
    English
This Course Includes
  • 1 Modules
  • 9 Lectures
  • 1 Quizzes
  • Full Lifetime Access
  • Certificate of Completion