- 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
1.Hands-On Machine Learning with Scikit-Learn, 2nd Edition2.PySpark Algorithms Book by Mahmoud Parsian3.Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman
- Scikit-Learn - Machine Learning in Python
- Apark-SKlearn
- Apache Spark
- Apache Spark Machine Learning
- handson-ml2
| 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% |
- Date: Wednesday, May 6, 2020
- Time: 7:35 PM - 9:10 PM, PST
- Date: Wednesday, June 10, 2020
- Time: 5:45 PM - 7:45 PM, PST
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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.
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It introduces topics that are often qualitative but that are now amenable to quantitative treatment.
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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.
The main focus of this class is to cover the following concepts:
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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
