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All Projects → mahmoudparsian → machine-learning-course

mahmoudparsian / machine-learning-course

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Machine Learning Course @ Santa Clara University

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machine-learningsparkclusteringlinear-regressionscikit-learnpysparksupervised-learninglogistic-regressionunsupervised-learningkmeans-clusteringspark-mldata-algorithmssanta-clara-universitypyspark-algorithms-book

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Spring 2020

Santa Clara University

Machine Learning

Machine Learning

Machine Learning Course @SCU

Course MSIS 2631: Machine Learning

  • Graduate School, Leavey School of Business
  • Department of Information Systems & Analytics
  • Class meeting dates:
    • Start: March 30, 2020
    • End: June 11, 2020
  • Class hours:
    • Monday 7:35 PM - 9:10 PM, PST
    • Wednesday 7:35 PM - 9:10 PM, PST
  • Instructor: Mahmoud Parsian
  • Class room: online
  • Office: 216AA, 2nd Floor, Lucas Hall (not used now due to covid-19)
  • Office Hours: by appointment

Required Books

Required Software:

Assignments

Assignment Percentage
Quiz #1 13%
Quiz #2 13%
Quiz #3 13%
Quiz #4 13%
Quiz #5 13%
Midterm Exam 15%
Final Exam 20%
Bonus 2%

Exams

Midterm Exam:

  • Date: Wednesday, May 6, 2020
  • Time: 7:35 PM - 9:10 PM, PST

Final Exam:

  • Date: Wednesday, June 10, 2020
  • Time: 5:45 PM - 7:45 PM, PST

Course Description

  • This course introduces participants to quantitative techniques and algorithms that are based on big data (numerical and textual) or are theoretical models of big systems or optimization that are currently being used widely in business.

  • It introduces topics that are often qualitative but that are now amenable to quantitative treatment.

  • The course will prepare participants for more rigorous analysis of large data sets as well as introduce machine learning models and data analytics for business intelligence.

Main Focus

The main focus of this class is to cover the following concepts:

  • Basic concepts of Machine Learning

    • Supervised learning
    • Unsupervised learning
    • Reinforcement learning

  • Linear Regression

    • scikit-learn
    • Spark ML
    • machine_learning_algorithms_from_scratch_SLR_sample_chapter.pdf

  • Logistic Regression

    • scikit-learn
    • Spark ML

  • Principal Component Analysis (PCA)

    • scikit-learn
    • Spark ML

  • Clustering

    • K-means
    • Latent Dirichlet allocation (LDA)
    • scikit-learn
    • Spark ML

  • Support Vector Machines (SVM)

    • scikit-learn
    • Spark ML

  • Frequent Pattern Mining

    • FP-Growth
    • PrefixSpan

  • Naive Bayes

    • scikit-learn
    • Spark ML

  • Bayesian Networks

    • scikit-learn
    • Spark ML

  • Decision Trees

    • scikit-learn
    • Spark ML

  • K-nearest neighbors algorithm (KNN)

    • scikit-learn

Machine Learning Syllabus

Machine Learning Project

Mahmoud Parsian's Latest Books:

PySpark Algorithms Book

PySpark Algorithms Book

Data Algorithms Book

Data Algorithms Book
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].