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    Machine Learning

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    Er Umesh Thoriya
    This document outlines a Machine Learning course at Gujarat Technological University. The course covers supervised and unsupervised learning techniques, including regression, classification, clustering, and dimensionality reduction. It also covers model selection, deep learning, feature representation, scalable machine learning, and recent trends in machine learning for IoT applications. The course aims to help students learn patterns from data, analyze machine learning algorithms, and apply techniques to IoT nodes. Assessment includes exams, projects, experiments, and a research paper. Prerequisites are data structures and probability/statistics courses.

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    Machine Learning

    Uploaded by

    Er Umesh Thoriya
    101 views2 pages
    This document outlines a Machine Learning course at Gujarat Technological University. The course covers supervised and unsupervised learning techniques, including regression, classification, clustering, and dimensionality reduction. It also covers model selection, deep learning, feature representation, scalable machine learning, and recent trends in machine learning for IoT applications. The course aims to help students learn patterns from data, analyze machine learning algorithms, and apply techniques to IoT nodes. Assessment includes exams, projects, experiments, and a research paper. Prerequisites are data structures and probability/statistics courses.

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    Original Title

    3710216_Machine Learning

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    This document outlines a Machine Learning course at Gujarat Technological University. The course covers supervised and unsupervised learning techniques, including regression, classification, clustering, and dimensionality reduction. It also covers model selection, deep learning, feature representation, scalable machine learning, and recent trends in machine learning for IoT applications. The course aims to help students learn patterns from data, analyze machine learning algorithms, and apply techniques to IoT nodes. Assessment includes exams, projects, experiments, and a research paper. Prerequisites are data structures and probability/statistics courses.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    101 views2 pages

    Machine Learning

    Uploaded by

    Er Umesh Thoriya
    This document outlines a Machine Learning course at Gujarat Technological University. The course covers supervised and unsupervised learning techniques, including regression, classification, clustering, and dimensionality reduction. It also covers model selection, deep learning, feature representation, scalable machine learning, and recent trends in machine learning for IoT applications. The course aims to help students learn patterns from data, analyze machine learning algorithms, and apply techniques to IoT nodes. Assessment includes exams, projects, experiments, and a research paper. Prerequisites are data structures and probability/statistics courses.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
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    of 2

    GUJARAT TECHNOLOGICAL UNIVERSITY

    Master of Engineering
    Subject Code: 3735904
    Semester – III
    Subject Name: Machine Learning

    Type of course:

    Prerequisite: Data Structures, Basics of Probability and Statistics

    Rationale: Machine learning is a method of data analysis that automates analytical model building. It is a
    branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and
    make decisions with minimal human intervention. This subject will help students to learn patterns and
    concepts from data without being explicitly programmed in various IOT nodes and also motivates them to
    design and analyse various machine learning algorithms and techniques with a modern outlook focusing on
    recent advances
    Teaching and Examination Scheme:

    Teaching Scheme Credits Examination Marks Total


    L T P C Theory Marks Practical Marks Marks
    ESE(E) PA (M) PA (V) PA (I)
    3 0 0 3 70 30 0 0 100

    Content:

    Sr. Content Total %


    No Hrs Weightage

    1 Supervised Learning (Regression/Classification) 10 15%


    Basic methods: Distance-based methods, Nearest-Neighbours,
    Decision Trees, Naive Bayes
    Linear models: Linear Regression, Logistic Regression, Generalized
    Linear Models
    Support Vector Machines, Nonlinearity and Kernel Methods
    Beyond Binary Classification: Multi-class/Structured Outputs,
    Ranking
    2 Unsupervised Learning 7 15%
    Clustering: K-means/Kernel K-means
    Dimensionality Reduction: PCA and kernel PCA
    Matrix Factorization and Matrix Completion
    Generative Models (mixture models and latent factor models)
    3 Evaluating Machine Learning algorithms and Model Selection, 6 20%
    Introduction to Statistical Learning Theory, Ensemble Methods
    (Boosting, Bagging, Random Forests)
    4 Sparse Modelling and Estimation, Modelling Sequence/Time-Series 9 20%
    Data, Deep Learning and Feature Representation Learning
    5 Scalable Machine Learning (Online and Distributed Learning) A 9 20%
    selection from some other advanced topics, e.g., Semi-supervised
    Learning, Active Learning, Reinforcement Learning, Inference in
    Graphical Models, Introduction to Bayesian Learning and Inference
    6 Recent trends in various learning techniques of machine learning and 5 10%
    classification methods for IOT applications. Various models for IOT
    applications.

    Page 1 of 2
    w.e.f. AY 2018-19
    GUJARAT TECHNOLOGICAL UNIVERSITY
    Master of Engineering
    Subject Code: 3735904

    References:
    1. Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press, 2012.
    2. The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer
    2009 (freely available online)
    3. Machine Learning in Action, Peter Harrington, Manning, dreamtech press
    4. Machine Learning for Big Data, Jason Bell, Wiley
    5. Machine Learning in Python, Michael Bowles, Wiley
    6. Machine Learning with TensorFlow for dummies, Matthew Scarpino, Wiley
    7. Python Machine Learning By Example, Yuxi Liu, Packt
    8. Advance Machine Learning with Python, John Hearty, Packt
    9. Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press
    10. Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2007.

    Course Outcome:

    After learning the course the students should be able to:

    1. Extract features that can be used for a particular machine learning approach in various IOT
    applications.
    2. To compare and contrast pros and cons of various machine learning techniques and to get an insight
    of when to apply a particular machine learning approach.
    3. To mathematically analyse various machine learning approaches and paradigms.

    List of Experiments:

     Minimum 10 experiments based on the contents.


     Mini Project in a group of max. 3 students
     Writing a research paper on selected topic from content with latest research issues in that topic

    Major Equipments:

    - Modern System with related software

    List of Open Source Software/learning website:

    https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-
    2/

    https://www.rstudio.com/online-learning/

    Page 2 of 2
    w.e.f. AY 2018-19

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