Syllabus 

 Course Material 

 Assignments 

 Class Project 

 Online Resources 

 Home

Course Syllabus

INSTRUCTOR

COURSE DESCRIPTION

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.

PREREQUISITES

CSC 401 and DSC 441

TEXTBOOKS & COURSE MATERIAL

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.

Required Text
	Machine Learning in Action, by Peter Harrington, Manning Publications, 2012.
Recommended Texts
	Python Data Science Essentials, Third Edition, by Alberto Boschetti and Luca Massaron, Packt Publishing, 2018.
	Python for Data Analysis, by Wes McKinney, 2nd Editions, O'Reilly, 2017.

GRADING & COURSE REQUIREMENTS

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%

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