{"id":14090,"date":"2020-08-27T06:44:38","date_gmt":"2020-08-27T06:44:38","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/csdlo6021-machine-learning-syllabus-for-cs-6th-sem-2018-pattern-mumbai-university-department-level-optional-course-2\/"},"modified":"2020-08-27T06:44:38","modified_gmt":"2020-08-27T06:44:38","slug":"csdlo6021-machine-learning-syllabus-for-cs-6th-sem-2018-pattern-mumbai-university-department-level-optional-course-2","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/csdlo6021-machine-learning-syllabus-for-cs-6th-sem-2018-pattern-mumbai-university-department-level-optional-course-2\/","title":{"rendered":"CSDLO6021: Machine Learning Syllabus for CS 6th Sem 2018 Pattern Mumbai University (Department Level Optional Course-2)"},"content":{"rendered":"

Machine Learning detailed syllabus scheme for Computer Engineering (CS), 2018 regulation has been taken from the MU<\/a> official website and presented for the Bachelor of Engineering students. For Course Code, Course Title, Test 1, Test 2, Avg, End Sem Exam, Team Work, Practical, Oral, Total, and other information, do visit full semester subjects post given below. <\/p>\n

For 6th Sem Scheme of Computer Engineering (CS), 2018 Pattern, do visit CS 6th Sem Scheme, 2018 Pattern<\/a>. For the Department Level Optional Course-2 scheme of 6th Sem 2018 regulation, refer to CS 6th Sem Department Level Optional Course-2 Scheme 2018 Pattern<\/a>. The detail syllabus for machine learning is as follows.<\/p>\n

Machine Learning Syllabus for Computer Engineering TE 6th Sem 2018 Pattern Mumbai University<\/h2>\n

Course Objectives:<\/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

\nStudents will be able to-<\/p>\n

    \n
  1. Gain knowledge about basic concepts of Machine Learning<\/li>\n
  2. Identify machine learning techniques suitable for a given problem<\/li>\n
  3. Solve the problems using various machine learning techniques<\/li>\n
  4. Apply Dimensionality reduction techniques.<\/li>\n
  5. Design application using machine learning techniques<\/li>\n<\/ol>\n

    Prerequisites:<\/h4>\n

    \nData Structures, Basic Probability and Statistics, Algorithms<\/p>\n

    Module 1<\/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

    Module 2<\/h4>\n

    \nIntroduction to Neural Network Introduction – Fundamental concept – Evolution of Neural Networks -Biological Neuron, Artificial Neural Networks, NN architecture, Activation functions, McCulloch-Pitts Model. 8<\/p>\n

    Module 3<\/h4>\n

    \nIntroduction to Optimization Techniques: Derivative based optimization- Steepest Descent, Newton method. Derivative free optimization- Random Search, Down Hill Simplex 6<\/p>\n

    Module 4<\/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

    Module 5<\/h4>\n

    \nLearning with Classification and clustering: 14<\/p>\n

      \n
    1. Classification: Rule based classification, classification by Bayesian Belief networks, Hidden Markov Models. Support Vector Machine: Maximum Margin Linear Separators, Quadratic Programming solution to finding maximum margin separators, Kernels for learning non-linear functions.<\/li>\n
    2. Clustering: Expectation Maximization Algorithm, Supervised learning after clustering, Radial Basis functions.<\/li>\n<\/ol>\n

      Module 6<\/h4>\n

      \nDimensionality Reduction: Dimensionality Reduction Techniques, Principal Component Analysis, Independent Component Analysis, Single value decomposition 8<\/p>\n

      Text Books:<\/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

      Reference Books:<\/h4>\n

      \n

        \n
      1. Han Kamber, Data Mining Concepts and Techniques, Morgann Kaufmann Publishers<\/li>\n
      2. Margaret.H.Dunham, Data Mining Introductory and Advanced Topics, Pearson Education<\/li>\n<\/ol>\n

        Internal Assessment:<\/b> Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when approx. 40% syllabus is completed and second class test when additional 40% syllabus is completed. Duration of each test shall be one hour. End Semester Theory Examination:<\/b><\/p>\n