Syllabus ICE 532: Adaptive Filter Theory (調適性濾波器理論) Time\Place: 0910-1200, Tuesday\EC3010 Instructor: 曾凡碩 (Fan-Shuo Tseng) Room: EC2016a Tel: 07-5252000-4478/4186 Email: [email protected] http://140.117.166.224/Course_ASP.htm TA: 謝崑岳 1 Syllabus This course is a graduate level course. The main topics cover Introduction (one week) Background review (2 weeks) Linear optimal filtering (2 weeks) Steepest descent (2 weeks) Least-mean-square algorithm (2 weeks) Least square (2 weeks) Recursive least-squares algorithm (2 weeks) Kalman filter (3 weeks) 2 Syllabus Grading policy Midterm: 25% Final: 25% Homework: 50% Do not cheat in any examination Do not copy the homework from your classmate If it was happened, you will totally share the grade of 100 Discussion is very encouraged 3 Syllabus Teaching style This course follows the slices provided by the instructor. Students are expected to do the homework when each unit is finished. There will be a midterm and final exam during this semester. All of these will be counted towards the final grade Class participate is strongly encouraged. Students who are very active will receive an additional bonus on his/her final grade Prerequisites: Knowledge of Matlab is required. Also, understanding of basic concept of digital signal process (DSP) are required 4 Syllabus Text book: S. Haykin, Adaptive Filter Theory, Pearson Education Taiwan Ltd, 2002. (main textbook). D. G. Manolakis, V. K. Ingle, S. M. Kogon, Statistical and Adaptive Signal Processing, McGraw-Hill Higher Education, 2000 (as a reference) T. Kailath, A. H. Sayed, and B. Hassi, Linear Estimation, Prentice-Hall, 2000. (as a reference) 5