{"id":29057,"date":"2025-04-14T14:39:49","date_gmt":"2025-04-14T09:09:49","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/314320-mathematics-for-machine-learning-syllabus-for-data-science-4th-sem-k-scheme-msbte-pdf\/"},"modified":"2025-04-14T14:39:49","modified_gmt":"2025-04-14T09:09:49","slug":"314320-mathematics-for-machine-learning-syllabus-for-data-science-4th-sem-k-scheme-msbte-pdf","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/314320-mathematics-for-machine-learning-syllabus-for-data-science-4th-sem-k-scheme-msbte-pdf\/","title":{"rendered":"314320: Mathematics for Machine Learning Syllabus for Data Science 4th Sem K Scheme MSBTE PDF"},"content":{"rendered":"

Mathematics for Machine Learning detailed Syllabus for Data Science (DS), K scheme PDF has been taken from the MSBTE<\/a> official website and presented for the diploma students. For Subject Code, Subject Name, Lectures, Tutorial, Practical\/Drawing, Credits, Theory (Max & Min) Marks, Practical (Max & Min) Marks, Total Marks, and other information, do visit full semester subjects post given below. <\/p>\n

For all other MSBTE Data Science 4th Sem K Scheme Syllabus PDF, do visit MSBTE Data Science 4th Sem K Scheme Syllabus PDF Subjects<\/a>. The detailed Syllabus for mathematics for machine learning is as follows.<\/p>\n

Rationale<\/h4>\n

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
\"Get<\/a>.<\/p>\n

Course Outcomes:<\/h4>\n

Students will be able to achieve & demonstrate the following COs on completion of course based learning<\/p>\n

    \n
  1. Use partial differentiation concept to obtain optimal solution.<\/li>\n
  2. Implement matrix concept to solve real life problems.<\/li>\n
  3. Build programs to implement basic operations based on vectors and tensors.<\/li>\n
  4. Evaluate numerical differentiation and integration functions.<\/li>\n
  5. Apply the linear programming problem concept to obtain optimal solution.<\/li>\n<\/ol>\n

    Unit I<\/h4>\n

    Partial Differentiation 1.1\tIntroduction to Derivative 1.2\tPartial derivative (Two variables): Introduction, Partial derivative of first order, second order and mixed order 1.3\tHomogeneous Function 1.4\tEuler’s theorem on homogeneous function (Two variables) 1.5\tMaxima and minima of function (Two variables) 1.6\tLagrange’s method of undetermined multipliers with one constraint (Two variables)\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Usin Chalk-Board Flipped Classroom Demonstratio\n<\/p>\n

    Unit II<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    Unit III<\/h4>\n

    Vectors and Tensors 3.1\tIntroduction, Definition of scalar and vector quantity, Representation of vector, Magnitude of vector, Component of vector, Direction ratio, Direction cosines 3.2\tTypes of vectors: Zero vector, Unit vector, Position vector, Equal vector, Negative vector. Parallel vector, Co-initial vector, Collinear vector 3.3\tAlgebra of vectors: Addition of vectors, Triangle law of vectors addition, Parallelogram law of vectors addition, Subtraction of vectors, Multiplication of vectors by scalar 3.4\tProduct of two vectors: Scalar (dot) product of two vectors, Projection of one vector on another vector, Angle between two vectors using scalar(dot) product, Properties of scalar(dot) product 3.5\tVector (cross)product of two vectors, Angle between two vectors using vector(cross) product, Properties of vector(cross) product 3.6\tScalar triple product of vectors 3.7\tTensor: Definition of tensors, Types of tensors, Rank of tensors, Algebra of tensors\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Usin Chalk-Board Demonstratio Flipped Classroom\n<\/p>\n

    Unit IV<\/h4>\n

    Numerical Differentiation and Integration 4.1\tIntroduction to numerical differentiation and integration 4.2\tDerivative using forward and backward interpolation 4.3\tNumerical integration using Trapezoidal rule 4.4\tNumerical integration using Simpson’s one third rule 4.5\tNumerical integration using Simpson’s three eight rule\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Usin Chalk-Board Flipped Classroom Presentations\n<\/p>\n

    Unit V<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    List of Experiments:<\/h4>\n
      \n
    1. Write a program to compute partial derivative. 2\tC<\/li>\n
    2. * Write a program to find maximum and minimum value of the function for two variables. 2\tC<\/li>\n
    3. Write a program to find maximum and minimum value of the function for three variables. 2\tC<\/li>\n
    4. Write a program to find a) Elementary row and column transformations using Python loops. b) Rank of a matrix. 2\tC<\/li>\n
    5. * Write a program to find inverse of a matrix by elementary transformation. 2\tC<\/li>\n
    6. * Write a program to solve system of linear equations. 2\tC<\/li>\n
    7. Write a program to calculate eigen values and eigen vector for given matrix of order 2. 2\tC<\/li>\n
    8. Write a program to calculate eigen values and eigen vector for given matrix of order 3. 2\tC<\/li>\n
    9. * Write a program to implement algebra of vectors like addition, subtraction and scalar multiplication. 2\tC<\/li>\n
    10. * Write a program to implement vectors operations like dot product, cross product and scalar triple product.<\/li>\n
    11. Write a program to implement basic algebraic operations on tensors like addition, subtraction. 2\tC<\/li>\n
    12. * Write a program to evaluate numerical differentiation for the given data. 2\tC<\/li>\n
    13. Write a program to evaluate numerical integration using Trapezoidal rule for the given data. 2\tC<\/li>\n
    14. * Write a program to evaluate numerical integration using Simpson’s one third rule for the given data. 2\tC<\/li>\n
    15. * Write a program to implement simplex method for 2 equations in 2 variables. 2\tC<\/li>\n<\/ol>\n

      Self Learning<\/h4>\n<\/p>\n

      Assignment<\/i><\/p>\n