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machine-learning-course

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Machine Learning Course @ Santa Clara University

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## Spring 2020

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[Santa Clara University](http://scu.edu/)

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[Machine Learning Course @SCU](https://www.scu.edu/business/ms-information-systems/curriculum/msis-courses/)

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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](https://www.scu.edu/business/isa/faculty/parsian/)
* Class room: online
* Office: 216AA, 2nd Floor, Lucas Hall ([not used now due to covid-19](https://www.cdc.gov/coronavirus/2019-nCoV/index.html))
* Office Hours: by appointment

## Required Books

* `1.`[Hands-On Machine Learning with Scikit-Learn, 2nd Edition](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/)
* `2.`[PySpark Algorithms Book by Mahmoud Parsian](https://www.amazon.com/dp/B07X4B2218/ref=sr_1_2)
	* [Buy from amazon.com -- PySpark Algorithms Book -- PDF Version](https://www.amazon.com/PySpark-Algorithms-Mahmoud-Parsian-ebook/dp/B07WQHTVCJ/)
	* [Buy from amazon.com -- PySpark Algorithms Book -- Kindle Version](https://www.amazon.com/PySpark-Algorithms-Version-Mahmoud-Parsian-ebook/dp/B07X4B2218/)
	* [Source code @github.com -- PySpark Algorithms Book by Mahmoud Parsian](https://github.com/mahmoudparsian/pyspark-algorithms)
* `3.`[Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman](http://infolab.stanford.edu/~ullman/mmds/book.pdf)

## Required Software:

* [Scikit-Learn - Machine Learning in Python](https://scikit-learn.org/stable/)
* [Apark-SKlearn](https://github.com/databricks/spark-sklearn)
* [Apache Spark](http://spark.apache.org/)
* [Apache Spark Machine Learning](http://spark.apache.org/mllib/)
* [handson-ml2](https://github.com/ageron/handson-ml2)

--------

## 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:](./exam_date/Spring_2020_Finals_Schedule.pdf) 
* 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		
	
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## [Machine Learning Syllabus](./syllabus/spring-2020/)

---------------------------

## [Machine Learning Project](./project/README.md)

---------------------------

## Mahmoud Parsian's Latest Books:

### PySpark Algorithms Book

### Data Algorithms Book

machine-learning-course

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