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 anjum chida utdallas edu 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 algorithms Assignments 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 Na ve 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 19 20 Oct 21 23 21 22 Oct 28 30 23 24 Nov 4 6 25 26 Nov 11 13 27 28 Nov 18 20 Perceptron Neural Networks Unsupervised Learning and Clustering Evaluation Metrics Ensemble Model Graphical Models Computational Learning Theory Reinforcement Learning Thanksgiving Holidays Nov 25 Dec 1 29 Dec 2 Review 30 Dec 4 EXAM 2 Required 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 Exam 2 Class Participation Assignment Average Homework 30 30 5 20 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 research notes and drafts until the project or assignment has been graded Obtain written authorization from your instructor prior to submitting a portion of academic work previously submitted for any academic exercise This includes an individual or group project submitted for another course or at another school Examinations ID requirement Accepting UTD Comet Card only Students will not be admitted to test without