Spring 2020

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Santa Clara University

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Machine Learning Course @SCU

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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

• 1.Hands-On Machine Learning with Scikit-Learn, 2nd Edition
• 2.PySpark Algorithms Book by Mahmoud Parsian
  • Buy from amazon.com -- PySpark Algorithms Book -- PDF Version
  • Buy from amazon.com -- PySpark Algorithms Book -- Kindle Version
  • Source code @github.com -- PySpark Algorithms Book by Mahmoud Parsian
• 3.Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman

Required Software:

• Scikit-Learn - Machine Learning in Python
• Apark-SKlearn
• Apache Spark
• Apache Spark Machine Learning
• handson-ml2

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Assignments

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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%

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Exams

Midterm Exam:

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

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Final Exam:

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

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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

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Machine Learning Syllabus

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Machine Learning Project

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Mahmoud Parsian's Latest Books:

PySpark Algorithms Book

Data Algorithms Book