CS5402: Introduction to Machine Learning Fall 2017 Course Syllabus Instructor: Dr. Zhaozheng Yin, email: [email protected] Office: Engineering Research Labs building 303 Office Hours: TuTh 1:00PM-2:00PM Class Schedule: TuTh 9:30AM - 10:45AM. Lecture Venue: Bertelsmeyer Hall B12A Course Description (3 credits): This course introduces foundational theories and techniques in machine learning. Topics will include basics of machine learning, learning theory, support vector machine, decision trees and ensemble methods. Students will implement course concepts in intensive programming assignments. Prerequisites: Good programming experience in Matlab, a “C” or better in both CS 2500, Math 3108, and one of Stat 3113, 3115, 3117 or 5643. Familiarity with basic concepts of linear algebra / matrices. Textbooks (recommended, but not required): The lectures will be based on the instructor’s slides, but the following textbooks are highly recommended: * Learning from Data. Yaser S. Abu-Mostaffa, et al. AMLBook, 2012 * Machine Learning. Tom Mitchell. McGraw-Hill Education, 1997. * The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Trevor Hastie, et al. Springer, 7th printing, 2013. * Pattern Recognition and Machine Learning. Christopher Bishop. Springer, 2007. * Machine Learning: a Probabilistic Perspective. Kevin Murphy. The MIT Press, 2012. * Pattern Classification (2nd ed.). R. Duda, P. Hart & D. Stork. Wiley. 2001. Grading: • • • •

Four Assignments: Final Project: Midterm Exam: Final Exam:

40% 20% 20% 20%

All grading will be determined based on a scale of: A: [90-100%], B: [80-90%), C: [70-80%), D: [6070%), F: [0-60%). Final grades in the course may be curved at the instructor’s discretion. Assignments: Assignments must be done individually and are due at the start of class on the date specified. Submissions by email are not allowed unless the instructor agrees in advance. Homework will be a combination of math problems and short programming problems. Use of Matlab is strongly encouraged for programming (it is faster for you to develop and for us to evaluate).

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Project Assignments: • There will be one computer project due at the end of the semester. This is much more serious programming effort than the assignments, and involve implementing end-to-end machine learning algorithms with several components. • Project submission includes codes and a written report. • Projects are to be submitted in Canvas, by the specified date and time. Your code must be in running order. In addition, 50% of the grade of the project will be based on the written report, which will include a problem statement, description of solution approach, rationale for any design decisions that were made, description of user-defined parameter settings, pictures of results produced, and a discussion of the results, including explanation of any deficiencies observed. • You can form a project team with two members for the project. You must clearly describe the work of each member in the project report. Late Homework/Project Policy: Any assignment/project turned in late, will incur a reduction of 33% in the final score, for each day (or part thereof) it is late. For example, if an assignment is up to 24 hours late, it incurs a penalty of 33%. Else if it is up to 48 hours late, it incurs a penalty of 66%. Academic Alert System The purpose of the Academic Alert System is to improve the overall academic success of students by improving communication among students, instructors and advisors; reducing the time required for students to be informed of their academic status; and informing students of actions necessary by them in order to meet the academic requirements in their courses. Disabilities If you have a documented disability and anticipate needing accommodations in this course, you are strongly encouraged to meet with the instructor as early as possible in the semester. You will need to request that the Disability Support Services staff send a letter to the instructor verifying your disability and specifying the accommodation you will need before the instructor can arrange your accommodation. Disability Support Services is located in 204 Norwood Hall, their phone number is 341-4211, and their E-mail is [email protected]. Academic Dishonesty Every student enrolled in this course is expected to be familiar with Missouri S&T's Student Academic Regulations, including the section on Conduct of Students which on pages 29-31 defines several forms of Academic Dishonesty such as cheating, plagiarism, and sabotage. Incidences of Academic Dishonesty will typically result in zero grades for the respective course components, notification of the student's advisor, the student's department chair, and the campus undergraduate studies office, and further academic sanctions may be imposed as well in accordance with the regulations. Note that those who allow others to copy their work are just as guilty of plagiarism and will be treated in the same manner. Although you are encouraged to talk to each other to understand the course material and assignment instructions, when it comes time to doing the assignments, every student (or group in the case of group programming assignments) is expected to submit their own original work. For programming, standard and publicly available code libraries may be used after seeking consent of the course instructor.

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