DSC 478 Programming Machine Learning Applications
Summary
The course will focus on the implementations of various data mining and machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. Additional open source machine learning and data mining tools may also be used as part of the class material and assignments. Students will develop hands on experience developing supervised and unsupervised machine learning algorithms and will learn how to employ these techniques in the context of popular applications such as automatic classification, recommender systems, searching and ranking, text mining, group and community discovery, and social media analytics.
Texts
We will use numerous online resources and documents throughout the course. The required and recommended textbooks are listed below. The resources directly relevant to topics covered in the course are listed in the Course Material section. Additional resources can be found on the Resources section.
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Required Text
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Machine Learning in Action, by Peter Harrington, Manning Publications, 2012. |
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Recommended Texts
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Python Data Science Essentials, Third Edition, by Alberto Boschetti and Luca Massaron, Packt Publishing, 2018.
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Python for Data Analysis, by Wes McKinney, 2nd Editions, O'Reilly, 2017. |
Grading
The structure and grading in the class will be centered around 4-5 assignments and a final project. The assignments will involve Python implementations of selected data mining techniques and their applications in various domains. The assignments will typically involve both programming components as well as problems related to the material covered in class. Some assignments may also involve the use of other open source data mining tools. These assignments must be done individually, unless otherwise specified. Late assignments will be penalized 10% per day (with weekends counting as one day).
The final project will be a more complex programming/implementation assignment that will involve integrating multiple concepts and techniques. Student will be able to choose from among several possible projects ideas or propose their own. More details on the final project are available in the Project section.
The final grade will be determined (tentatively) based on the following components:
- Assignments = 65%
- Final Project = 35%
The general grading scheme will be based on a curve. At the end of the quarter, some adjustments may be made based on overall class performance as well as signs of individual effort. Plusses and minuses will be given at the high/low ends of each grade range.
Prerequisites
Tentative List of Topics
The following issues and topics will be covered throughout the course. Many of these topics will be revisited several times during the course in a variety of contexts.
- Data Mining and Knowledge Discovery
- The KDD process and methodology
- Data preparation for knowledge discovery
- Overview of data mining and Machine Learning techniques
- Review of Python and overview of Python tools for Data Analysis
- Supervised Techniques
- Classification and Prediction using K-Nearest-Neighbor
- Classifying with Probability Theory; Naïve Bayes
- Building Decision Trees
- Regression models
- Evaluating predictive models
- Ensemble Models: Bagging and Boosting
- Unsupervised Learning
- Clustering using K-Means
- Hierarchical Clustering
- Association Rule discovery
- Principal Component Analysis and Dimensionality Reduction
- Singular Value Decomposition
- Advance Topics (if time permits)
- Matrix Factorization
- Support Vector Machines
- Search and Optimization Techniques
- Markov Models
Possible Applications (covered throughout the course)
- Collaborative Recommender Systems
- Text Categorization
- Predictive User Modeling
- Concept Discovery from Documents
- Finding groups using social or behavioral data
- Building predictive models for target marketing
- Customer or user segmentation
This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.
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Students complete the evaluation online in CampusConnect.
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have any questions be sure to consult with your professor.
All students are expected to abide by the University's Academic Integrity Policy which prohibits cheating and other misconduct in student coursework. Publicly sharing or posting online any prior or current materials from this course (including exam questions or answers), is considered to be providing unauthorized assistance prohibited by the policy. Both students who share/post and students who access or use such materials are considered to be cheating under the Policy and will be subject to sanctions for violations of Academic Integrity.
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