skilldevelopment.cpe@lpu.co.in

What you'll learn

Description: This course is designed to: ?Provide a foundational understanding of linear algebra, calculus, and probability concepts essential for machine learning. ?Equip learners with practical skills in numerical optimization, dimensionality reduction, and matrix algebra through Python implementations. ?Develop a strong theoretical base in advanced topics such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM). ?Enable learners to apply mathematical and computational concepts to solve real-world problems in machine learning. ?Foster critical thinking and problem-solving skills by exploring advanced matrix applications, classification metrics, and optimization techniques. ?Prepare learners to confidently tackle challenges in machine learning and data science with a robust mathematical and programming toolkit. Learning Outcome: ?Understand the fundamental concepts of linear algebra, calculus, and probability, and how they are applied in machine learning. ?Gain practical experience in implementing advanced techniques such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA) using Python. ?Learn to apply numerical optimization techniques, such as Gradient Descent, to improve machine learning models. ?Develop the ability to handle real-world machine learning challenges through effective use of matrix algebra, classification metrics, and optimization methods. ?Acquire knowledge of key machine learning algorithms like Support Vector Machines (SVM) and their practical applications. ?Build a solid foundation in mathematical and computational problem-solving, preparing learners to excel in the field of machine learning and data science. ?Gain a deeper understanding of how mathematical and statistical concepts can drive decisions in both academic and professional environments.

  • 1.Vectors in Machine Learning 2.Basics of Matrix Algebra 3.Vector Space, Subspace, Basis, and Dimension
  • 1.Linear Transformations 2.Norms and Spaces 3.Orthogonal Complement and Projection Mapping 4.Eigenvalues and Eigenvectors 5.Special Matrices and Their Properties
  • 1.Spectral Decomposition 2.Singular Value Decomposition (SVD) 3.Low-Rank Approximations 4.Python Implementation of SVD and Low-Rank Approximation
  • 1.Principal Component Analysis (PCA) 2.Python Implementation of PCA 3.Linear Discriminant Analysis (LDA) 4.Python Implementation of LDA
  • 1.Least Square Approximation and Minimum Normed Solution 2.Linear and Multiple Regression 3.Logistic Regression
  • 1.lassification Metrics 2.Gram-Schmidt Process 3.Polar Decomposition 4.Minimal Polynomial and Jordan Canonical Form 5.Applications of Matrices in Machine Learning
  • 1.Concepts of Gradient, Jacobian, and Chain Rule 2.Change of Variables
  • 1.Calculus in Python 2.Convex Sets and Convex Functions 3.Properties of Convex Functions 4.Introduction to Optimization
  • 1.Numerical Optimization Techniques 2.Gradient Descent and Other Optimization Algorithms in Machine Learning
  • 1.Optimization Using Python 2.Review of Probability, Bayes Theorem, and Random Variables 3.Expectation and Variance
  • 1.Discrete and Continuous Distribution Functions 2.Joint Probability and Covariance 3.Introduction to Support Vector Machines (SVM) 4.Error-Minimizing Linear Programming Problem (LPP)
  • 1.Lagrangian Multiplier Method 2.Concepts of Duality 3.Hard and Soft Margin Classifier 4.SVM Implementation in Python

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Dr. Nitin K Mishra
Professor

Dr. Nitin K. Mishra, a distinguished academic and educator whose career spans nearly 15 years of teaching excellence and 7 years of impactful research at Lovely Professional University, Phagwara, Punjab, India. Dr. Mishra’s contributions have been recognized through multiple milestone awards, reflecting his dedication to teaching, research, and academic growth. As a celebrated educator, he brings a wealth of expertise in mathematics, particularly in inventory management and supply chain coordination, earning accolades such as the Best Paper Award at the 4th International Conference on Computing Sciences.