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UTD CS 6375 - Machine Learning

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Course Syllabus Course Information: - Course Number: CS6375, Section 002 (M/W 1:00 pm – 2:15 pm, ECSW 1.365) - Course Title: Machine Learning - Credit Hours: 3 - Term: Fall 2019 Professor Contact Information: - Name: Anjum Chida - Phone: (972) 883-2185 - Office Location: ECSS 4.230 - Office Hours: T/Th 10:00 am – 12 noon (or by appointment) - Email: [email protected] TA Contact Information - Name: - Office Location: -Office Hours: - Email: Course Pre-Requisites, co-requisites, and/or other restrictions: CS5343 Algorithm Analysis and Data Structures. You are expected to have basic programming skills as well as knowledge of elementary data structures and probability theory Course Description: The main objective of this course is to introduce students to machine learning, the study of computer systems that improve their performance automatically through experience. Students will learn the latest machine learning algorithms and models that constitute typical machine learning systems. They will also gain the necessary foundations and background to both build practical machine learning systems and conduct research in machine learning. Student Learning Objectives/Outcomes Ability to understand and apply basic learning algorithms Ability to understand and apply computational learning theories Ability to understand and apply advanced learning algorithmsAssignments & Academic Calendar Assignments will be through eLearning. For academic calendar see: http://www.utdallas.edu/academiccalendar/files/Academic_Calendar_Fall_2019.pdf Class Date Material Covered 1, 2 Aug 19, 21 Introduction Linear Regression 3,4 Aug 26, 28 Induction Learning 5, 6 Sep 2*, 4 Holiday Decision Tree 7, 8 Sep 9, 11 Point Estimation 9, 10 Sep 16, 18 Naïve Bayes Logical Regression 11, 12 Sep 23 , 25 Support Vector Machines 13, 14 Sep 30, Oct 2 Instance Based Learning Review 15 Oct 7 EXAM 1 16, 17, 18 Oct 9, 14, 16 Perceptron ; Neural Networks 19, 20 Oct 21, 23 Unsupervised Learning and Clustering 21, 22 Oct 28, 30 Evaluation Metrics Ensemble Model 23, 24 Nov 4, 6 Graphical Models 25, 26 Nov 11, 13 Computational Learning Theory 27, 28 Nov 18, 20 Reinforcement Learning Thanksgiving Holidays – Nov 25-Dec 1 29 Dec 2 Review 30 Dec 4 EXAM 2Required Textbooks and Materials Machine Learning by Tom Mitchell. Machine Learning: a Probabilistic Perspective by Kevin Murphy. Pattern Recognition and Machine Learning by Christopher M. Bishop. Tentative Test Dates: Exam 1: October 7th CS_6375_002_Exam _1 Exam 2: December 4th CS_6375_002_Exam _2 *All examinations will be in Testing Center (https://ets.utdallas.edu/testing-center). Seats will fill up quickly; students are encouraged to register for ALL three of their exams early in advance during the first two weeks of semester. *Failure to take the exam due to incorrect registration or absence will result in score of zero for that exam, NO EXCEPTIONS. Grading Policy: The grade will be determined as described below. No other bonus work, make-up work, dropped scores, or other means of raising your grade should be expected. At the end of the semester, it is possible that grades may be curved, but a curve should not be expected. Exam 1 30% Exam 2 30% Class Participation 5% Assignment Average 20% Homework 15% Letter grades are determined using the standard 10-point range for each letter, then dividing this range into three equal parts to determine the +/- designation. Attendance Policy: Attendance will be taken in class. Missing three consecutive classes without approval by the instructor will automatically result in one letter grade drop and missing four consecutive classes will result in F in class. Attendance shall require access to elearning during class period.Course & Instructor Policies: Assignments and projects must be turned in on time. Each day late will result in a deduction of 10% points. It is your responsibility to upload your work early enough to avoid possible problems uploading to eLearning. It is your responsibility to ensure that you have submitted the correct items. It is recommended that you double-check your submission to ensure it is correct. Exams must be taken on time. Exceptions require advance approval by the instructor. It is up to the instructor to determine whether an exception will be made, and will depend largely on proof of extraordinary circumstances. Otherwise, a missed exam will either incur a substantial penalty or be recorded as a zero. Exams have time limits. Students who continue to write on the exam after time is called or who start writing before the exam begins are subject to a penalty. Students are expected to attend all class lectures. If absent, the student is still responsible for any material covered or anything said which the student missed. All assignments, projects and exams are to be individual efforts. You are not to collaborate with other students, or to discuss solutions with other students prior to submission. Copying of assignments, projects and exams, in whole or in part, from other students in this semester or previous semesters will be considered to be an act of scholastic dishonesty. Grades are not based on needs or consequences, but are based only on performance. UT Dallas Syllabus Policies and Procedures The information contained in the following link constitutes the University’s policies and procedures segment of the course syllabus. Please go to http://go.utdallas.edu/syllabus-policies for these policies. These descriptions and timelines are subject to change at the discretion of the Professor. Syllabus Addendum Each student in this course is expected to exercise independent scholarly thought, expression and aptitude. This addendum to the course syllabus is provided to assist you in developing and maintaining academic integrity while seeking scholastic success. General Comments: - All academic exercises (including assignments, essays, laboratory experiments and reports, examinations, etc.) require individual, independent work. Any exception(s) will be clearly identified. - Be sure your name or identifying number is on your paper. - Complete and turn in academic exercises on time and in the required format (hardcopy, electronic, etc.). - Retain confirmation of document delivery if submitted electronically.- Retain all


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