Mathematics for Machine Learning detailed Syllabus for Artificial Intelligence (AI), K scheme PDF has been taken from the MSBTE 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.
For all other MSBTE Artificial Intelligence 4th Sem K Scheme Syllabus PDF, do visit MSBTE Artificial Intelligence 4th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for mathematics for machine learning is as follows.
Rationale
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Course Outcomes:
Students will be able to achieve & demonstrate the following COs on completion of course based learning
- Use partial differentiation concept to obtain optimal solution.
- Implement matrix concept to solve real life problems.
- Build programs to implement basic operations based on vectors and tensors.
- Evaluate numerical differentiation and integration functions.
- Apply the linear programming problem concept to obtain optimal solution.
Unit I
Partial Differentiation 1.1 Introduction to Derivative 1.2 Partial derivative (Two variables): Introduction, Partial derivative of first order, second order and mixed order 1.3 Homogeneous Function 1.4 Euler’s theorem on homogeneous function (Two variables) 1.5 Maxima and minima of function (Two variables) 1.6 Lagrange’s method of undetermined multipliers with one constraint (Two variables)
Suggested Learning Pedagogie
Lecture Usin Chalk-Board Flipped Classroom Demonstratio
Unit II
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Unit III
Vectors and Tensors 3.1 Introduction, Definition of scalar and vector quantity, Representation of vector, Magnitude of vector, Component of vector, Direction ratio, Direction cosines 3.2 Types of vectors: Zero vector, Unit vector, Position vector, Equal vector, Negative vector. Parallel vector, Co-initial vector, Collinear vector 3.3 Algebra 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 Product 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 Vector (cross)product of two vectors, Angle between two vectors using vector(cross) product, Properties of vector(cross) product 3.6 Scalar triple product of vectors 3.7 Tensor: Definition of tensors, Types of tensors, Rank of tensors, Algebra of tensors
Suggested Learning Pedagogie
Lecture Usin Chalk-Board Demonstratio Flipped Classroom
Unit IV
Numerical Differentiation and Integration 4.1 Introduction to numerical differentiation and integration 4.2 Derivative using forward and backward interpolation 4.3 Numerical integration using Trapezoidal rule 4.4 Numerical integration using Simpson’s one third rule 4.5 Numerical integration using Simpson’s three eight rule
Suggested Learning Pedagogie
Lecture Usin Chalk-Board Flipped Classroom Presentations
Unit V
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List of Experiments:
- Write a program to compute partial derivative. 2 C
- * Write a program to find maximum and minimum value of the function for two variables. 2 C
- Write a program to find maximum and minimum value of the function for three variables. 2 C
- Write a program to find a) Elementary row and column transformations using Python loops. b) Rank of a matrix. 2 C
- * Write a program to find inverse of a matrix by elementary transformation. 2 C
- * Write a program to solve system of linear equations. 2 C
- Write a program to calculate eigen values and eigen vector for given matrix of order 2. 2 C
- Write a program to calculate eigen values and eigen vector for given matrix of order 3. 2 C
- * Write a program to implement algebra of vectors like addition, subtraction and scalar multiplication. 2 C
- * Write a program to implement vectors operations like dot product, cross product and scalar triple product.
- Write a program to implement basic algebraic operations on tensors like addition, subtraction. 2 C
- * Write a program to evaluate numerical differentiation for the given data. 2 C
- Write a program to evaluate numerical integration using Trapezoidal rule for the given data. 2 C
- * Write a program to evaluate numerical integration using Simpson’s one third rule for the given data. 2 C
- * Write a program to implement simplex method for 2 equations in 2 variables. 2 C
Self Learning
Assignment
- Collect five linear programming problems that can be solved graphically. Draw graph, identify the feasible and determine the optimal solution.
- Collect data set of different types of functions such as polynomial, trigonometric, logarithmic, exponential f of two variables. Calculate the partial derivatives of first order, second order and mixed order for each function
- Solve five examples to find addition, subtraction, scalar product and cross product of given vectors.
- Solve five examples to find the eigen values and eigen vector of matrix of order two and three.
- Solve five examples on numerical differentiation and integration.
Laboratory Equipment
For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
Learning Materials
- H. K. Dass, Er. Rajnish Verma Higher Engineering Mathematics S. Chand Technical, ISBN: 9788121938907
- K.Nageswara Rao, Shaikh Akbar Python Programming Scitech Publication(India) Pvt. Ltd. ISBN:9789385983450
- Grewal B. S. Higher Engineering Mathematics Tata McGraw Hill Education, New Delhi, ISB 9789386173522
- A. C. Shrivastava, P. K. Shrivastava Engineering Mathematics PHI Learning, New Delhi, ISBN:9788120342
- Mark Lutz Learning Python O’Reilly Publication ISBN-13: 978067232978
Learning Websites
- https://atozmath.com/default.aspx Online Learning Initiative for Mathematics Problems with Solutions
- https://www.w3schools.com/ai/ai_mathematics.asp Machine Learning Mathematics
- https://www.geeksforgeeks.org/machine-learning-mathematics/ Machine Learning Mathematics
- https://docs.python.org/3/tutorial/index.html The Python Tutorial
- https://onlinecourses.nptel.ac.in/noc21_ma38/preview NPTEL Course
- https://www.purplemath.com/index.htm Foundational Mathematics to improve learning
- https://mathworld.wolfram.com/ Extensive mathematical resource with detailed explanations
- https://www.khanacademy.org/math Mathematical concepts through video lectures
For detail Syllabus of all other subjects of Artificial Intelligence, K scheme do visit Artificial Intelligence 4th Sem Syllabus for K scheme.
For all Artificial Intelligence results, visit MSBTE Artificial Intelligence all semester results direct links.