CSCI 379 (ASU) and MA 440 (ERAU) – Data Mining and Visualization

Instructor: Dr. Matt Iklé (ASU); Co-instructor: Dr. Greg Spradlin (ERAU)

GENERAL INFORMATION

The National Science Foundation sponsored this course under contract IUSE1626602. The instructor and students have an obligation to collect learning assessment data to fulfill the research objectives of the NSF sponsored projects. The goal of this course is to learn how to use the advanced mathematics language and computation tools to solve real-world problems. The topics of the course cover interdisciplinary problems whose solutions heavily depend on data mining and visualization. Students will gain hands-on experience on how to use software tools to analyze large data sets. We will be joined remotely by Dr. Greg Spradlin’s class from Embry-Riddle Aeronautical University in Daytona Beach, Florida.

The course will be delivered using a blended learning model that includes multimedia instructional materials, live problem-solving sessions, and team projects.

Zoom Meeting Information:https://us02web.zoom.us/j/2047021638?pwd=a0hEeU9INy9ncjBBbGhSc0h5Ty9lQT09Meeting ID: 204 702 1638Passcode: datamining

Course time: Monday, Wednesday, Friday, 12:00 PM – 12:50 PM MT (2:30PM-3:20PM ET for ERAU students)Location: POR 235Office hours, Monday, and Friday, 1:20 PM – 1:50 PM but may change over the course of the semester

Prerequisite: Statistics and programming experience in R, Python, C/C++, Java, or MATLAB or approval from instructor.

Required Textbook: Aurélien Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O’Reilly Media, Inc., Sebastopol, 2017. Online resources available at https://datamined.wordpress.com

GRADING Exam 20 % Homework 20% Team Project 40 %Participation 20 %

STUDENT LEARNING OUTCOMES AND COURSE OBJECTIVES

CONTENT OUTLINE: Introduction to the Data Mining Cycle Data Preparation, Cleaning, and Preprocessing Introduction to Data Visualization Classification methods

Student Learning Outcomes

Relevant Program Goal Assessment Measures

Students will make sense of data by explorative statistics & visualization.

1, 2, 4 Exam, team projects.

Students will program broad variety of real-world applications of deep learning.

1, 2, 4 Exam, team projects.

Students will demonstrate understanding of three or more classic data mining methods (e.g., decision tree, NN, Bayesian, neural network, support vector machine) and be familiar with deep neural networks.

1, 2, 4 Exams, team projects.

Students will critique the proper algorithms and tools based on characteristics of training datasets and thestrengths and limitations of data mining methods.

1, 2, 4 Exams, team projects.

Students will validate models, evaluate results, and measure the uncertainty of the conclusions.

1, 2, 4 Exams, team projects.

Students will program using data mining Python libraries.

1, 2, 4 Exams, team projects.

Students will gain teamwork and hands-on experience to identify data sources, retrieve and cleanse data, and use of software tools to solve data enabled research problems.

1, 2, 3, 4 Exams, team projects.

Students will communicate data mining results effectively with data visualization tools.

1, 2, 4 Exams, team projects.

Clustering Methods Advanced Techniques including a brief survey of Deep Neural Networks and

DNN libraries

ATTENDANCE POLICY: Your attendance at all lectures and exams is expected. If you miss a class, you are responsible for material that you miss. Attendance will be used to determine borderline grades at the end of the semester.

INCOMPLETES: Incompletes are given only under extraordinary circumstances and only when the student has substantially completed the course work with a passing grade but cannot finish the course for a legitimate reason.

CHEATING: Cheating is defined as submitting work under your name that was not done entirely by you, from memory. Cheating on homework and exams will not be tolerated. Cheating will lead to expulsion from the course with a grade of F.

ADA STATEMENT: Adams State University complies with the Americans with Disabilities Act and Section 504 of the Rehabilitation Act. Adams State University is committed to achieving equal educational opportunities, providing students with documented disabilities access to all university programs, services and activities. In order for this course to be equally accessible to all students, different accommodations or adjustments may need to be implemented. The Office of Accessibility Services (OAS) is located in Richardson Hall 3-100, or available at OAS@adams.edu, and 719-587-7746. They are your primary resource on campus to discuss the qualifying disability, help you develop an accessibility plan, and achieve success in your courses this semester. They may provide you with letters of accommodation, which can be delivered in two ways. You may give them to me in person, or have the Office of Accessibility Services email them. Please make an appointment with their office as early as possible this semester so that we can discuss how potential accommodations can be provided and carried out for this course. If you have already received letters of accommodation for this course from OAS, please provide me with that information privately so that we can review your accommodations together and discuss how best to help you achieve equal access in this course this semester.

STATEMENT REGARDING ACADEMIC FREEDOM AND RESPONSIBILITY: Academic freedom is a cornerstone of the University. Within the scope and content of the course as defined by the instructor, it includes the freedom to discuss relevant matters in the classroom. Along with this freedom comes responsibility. Students are encouraged to develop the capacity for critical judgment and to engage in a sustained and independent search for truth. Students are free to take reasoned exception to the views offered in any course of study and to reserve judgment about matters of opinion, but they are responsible for learning the content of any course of study for which they are enrolled.

Face coverings and social distancing in the classroomStudents are required to correctly wear face coverings at all times (covering nose and mouth) and maintain social distancing (minimum of six feet between individuals in traditional classrooms, or, in instructional laboratories and similar settings, only a few

minutes in closer proximity when absolutely necessary to achieve learning objectives). In addition, students should not share pens, phones or other personal items. Students who are feeling ill or experiencing symptoms such as sneezing, coughing, or a higher than normal temperature will be excused from class and must stay at home.Instructors have the right to and will ask individuals who do not comply with mask and social distancing requirements to leave class in the interest of everyone's health and safety. In the event that a student refuses to comply, they will be considered in violation of the student code of conduct and will be reported to Student Affairs. Additionally, students may be asked to wipe down areas/common equipment when necessary, including, but not limited to, during class and/or after classes end. Again, this will be in compliance of the student code of conduct. The University asks that we all demonstrate the Grizzly spirit by following these and all health guidelines and requirements. We expect our university community to set the example and keep ourselves and the members of the SLV community in which we live, work and learn, safe.

EMERGENCY EVACUATION PROCEDURES:In case of fire or any other event requiring immediate evacuation, please evacuate by proceeding down the main stairwell, if it is safe to do so, and exit out the main east doors. If not safe please exit the building via any safe route. Upon evacuation, our gathering spot will be in the large grass area immediately south of McDaniel Hall, shown as area 47 in the diagram below.

Unit 1 Introduction to the Data Mining Cycle Homework

August 24 Class 1 Course and Content Introduction; What is Data Mining/Machine Learning?

26 Class 2 Data Mining Cycle and Challenges28 Class 3 Data Types and Data Set QualityAugust 31 Class 4 Measuring Data Similarity and DissimilaritySeptember 2 Class 5 Review

Unit 2 Data Preparation and Visualization4 Class 6 Data Preprocessing Hw 1 Due7 Class 7 Summary Statistics9 Class 8 Introduction to Data Visualization11 Class 9 Validation and Overfitting Hw 2 Due14 Class 10 Review

Unit 2 Classification16 Class 11 Decision Trees21 Class 12 Decision Trees HW 3 due23 Class 13 Naïve Bayes, Part 125 Class 14 Naïve Bayes, Part 228 Class 15 Review30 Class 16 Linear and Logistic Regression HW 4 dueOctober 2 Class 17 Linear Support Vector Machine (SVM)5 Class 18 Nonlinear SVM7 Class 19 K- Nearest Neighborhood Classifier (KNN) HW 5 due9 Class 20 Review

Unit 3 Ensemble Mathods12 Class 21 Ensemble Methods and AdaBoost Examples14 Class 22 Classifier Evaluation16 Class 23 Review19 Class 24 Exam21 Class 25 Artificial Neural Network23 Class 26 Motivation of Deep Learning and Nonlinear Challenges26 Class 27 Gradient Based Learning28 Class 28 Review30 Class 29 Project Teams Formation and Project AssignmentsNovember 2 Class 30 Project Questions, Clustering, Review4 Class 31 Project Questions, Clustering, Review6 Class 32 Project Questions, Clustering, Review9 Class 33 Project Questions, Advanced Topics, Review11 Class 34 Project Questions, Advanced Topics, Review13 Class 35 Project Questions, Advanced Topics, Review16 Class 36 Project Questions, Advanced Topics, Review18 Class 37 Project Questions, Advanced Topics, Review20 Class 38 Project Questions, Advanced Topics, Review23 Class 39 Project Questions, Advanced Topics, Review

Thanksgiving BreakDecember 3 Final Presentations 8:00-9:50 MT/10:00-11:50 ET