Section outline

  • Dear Learners,
    Wel-Come to the Course on Machine Learning.It has Following Course Objectives & Course Outcomes:

    Prerequisites: Data Mining, Discrete Mathematics, Database

    Course Objectives:

    1.                  To understand the need for machine learning for various problem solving

    2.                  To understand the nature of the problem and apply machine learning algorithms.

    3.                   To study the various supervised, semi-supervised and unsupervised learning algorithms in

                   machine learning

    4.                  To understand the latest trends in machine learning

    5.                  To design appropriate machine learning algorithms for problem solving

    Course Outcomes (COs): At the end of this course, students will be able to,

    CO No.

    Statement of Course Outcome

    Bloom’s Taxonomy

    Level

    Descriptor

    CO1

     Understand different learning based applications.

    2

    Understand

    CO2

    Apply different pre-processing methods to prepare training data set for machine learning

    3

    Apply

    CO3

    Apply the  Regression Techniques to various problems

    3

    Apply

    CO4

    Apply the  Bayesian algorithm to various problems

    3

    Apply

    CO5

    Apply  the classification &  ensemble techniques.

    3

    Apply

    CO6

    Ability to apply Clustering techniques for data.

    3

    Apply

    Text Books:

     

    Sr. No.

    Authors

    Title

    Edition

    Year

    Publication

    1

    Giuseppe Bonaccorso

    Machine Learning Algorithms

     

     

    Packt Publishing Limited

    2

    Tom M. Mitchell

    Machine Learning

     

    2013

    McGraw-Hill Education (India) Private Limited,

    3

    Josh Patterson, Adam Gibson

    Deep Learning: A Practitioners Approach

     

    2017

    O‟REILLY

    References Books:

    Sr. No.

    Authors

    Title

    Edition

    Year

    Publication

    1

    Ethem Alpaydin

    Introduction to Machine Learning

     

     

    The MIT Press

    2

    Stephen Marsland

    Machine Learning: An Algorithmic Perspective

     

     

    CRC Press

    3

    Ethem Alpaydin

    Introduction to Machine Learning

    2nd

     

    PHI

    4

    Peter Flach

    Machine Learning: The Art and Science of Algorithms that Make Sense of Data

     

     

    Cambridge University Press

    5

    Nikhil Buduma

    Fundamentals of Deep Learning

     

     

    O‟REILLY publication

     

    E-Resources:

     

    Sr. No.

    Link

    1

    https://nptel.ac.in/courses/106105152   

    2

    https://ocw.mit.edu/courses/6-036-introduction-to-machine-learning-fall-2020/

     

    Thanks & Regards,
    Dr. T.Bhaskar
    Associate Professor(Computer Engg),Sanjivani College of Engineering,Kopragon
    Google-Site: https://sites.google.com/view/bhaskart/ug-notes/datamining-warehousing
    Moodle-Site: https://proftbhaskar.gnomio.com/course/view.php?id=3 (Log in as Guest)
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