{"id":70712,"date":"2023-03-15T15:21:55","date_gmt":"2023-03-15T07:21:55","guid":{"rendered":"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&#038;p=70712"},"modified":"2024-01-19T13:56:49","modified_gmt":"2024-01-19T05:56:49","slug":"information-theory-107-academic-year-college-of-electrical-and-computer-engineering-prof-po-ning-chen","status":"publish","type":"course_page","link":"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/college-of-electrical-and-computer-engineering\/information-theory-107-academic-year-college-of-electrical-and-computer-engineering-prof-po-ning-chen","title":{"rendered":"Information Theory \u2013 107 Academic Year | College of Electrical and Computer Engineering Prof. Po-Ning Chen"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"70712\" class=\"elementor elementor-70712\">\n\t\t\t\t\t\t\t\t\t<section class=\"penci-section penci-disSticky penci-structure-10 elementor-section elementor-top-section elementor-element 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href=\"https:\/\/ocw.nycu.edu.tw\/\">Homepage<\/a><\/span><\/span><\/p>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"penci-section penci-disSticky penci-structure-20 elementor-section elementor-top-section elementor-element elementor-element-0025485 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0025485\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"penci-ercol-50 penci-ercol-order-1 penci-sticky-ct    elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-0446ed9\" data-id=\"0446ed9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2f5840a elementor-widget elementor-widget-penci-info-box\" data-id=\"2f5840a\" data-element_type=\"widget\" data-widget_type=\"penci-info-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div id=\"penci_info_box_27962\" class=\"penci-block-vc penci-info-box penci-ibox-float-left penci-view-default penci-shape-circle\">\r\n\t\t\t<div class=\"penci-ibox-inner\">\r\n\t\t\t\t\t\t\t\t<div class=\"penci-ibox-icon penci-ibox-icon--icon penci-icon penci-tibox-text\"><span class=\"penci-ibox-icon-fa\"><\/span><\/div>\t\t\t\t<div class=\"penci-ibox-content-wrap\">\r\n\t\t\t\t\t\t\t\t\t\t<h3 class=\"penci-ibox-title\">Information Theory - 107 Academic Year<\/h3>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c6381f4 elementor-widget elementor-widget-eael-adv-tabs\" data-id=\"c6381f4\" data-element_type=\"widget\" data-widget_type=\"eael-adv-tabs.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t        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0-48-21.5-48-48V112c0-26.5 21.5-48 48-48h48V12c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v52h128V12c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v52h48c26.5 0 48 21.5 48 48zm-48 346V160H48v298c0 3.3 2.7 6 6 6h340c3.3 0 6-2.7 6-6z\"><\/path><\/svg>                                                            \n                                                            <span class=\"eael-tab-title  title-after-icon\">Course Calendar<\/span>                            \n                                                    <\/li>\n                                            <li id=\"handout\" class=\"inactive eael-tab-item-trigger\" aria-selected=\"false\" data-tab=\"5\" role=\"tab\" tabindex=\"-1\" aria-controls=\"handout-tab\" aria-expanded=\"false\">\n                            \n                                                                <svg class=\"e-font-icon-svg e-fas-book\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M448 360V24c0-13.3-10.7-24-24-24H96C43 0 0 43 0 96v320c0 53 43 96 96 96h328c13.3 0 24-10.7 24-24v-16c0-7.5-3.5-14.3-8.9-18.7-4.2-15.4-4.2-59.3 0-74.7 5.4-4.3 8.9-11.1 8.9-18.6zM128 134c0-3.3 2.7-6 6-6h212c3.3 0 6 2.7 6 6v20c0 3.3-2.7 6-6 6H134c-3.3 0-6-2.7-6-6v-20zm0 64c0-3.3 2.7-6 6-6h212c3.3 0 6 2.7 6 6v20c0 3.3-2.7 6-6 6H134c-3.3 0-6-2.7-6-6v-20zm253.4 250H96c-17.7 0-32-14.3-32-32 0-17.6 14.4-32 32-32h285.4c-1.9 17.1-1.9 46.9 0 64z\"><\/path><\/svg>                                                            \n                                                            <span class=\"eael-tab-title  title-after-icon\">Course Handout<\/span>                            \n                                                    <\/li>\n                                    <\/ul>\n            <\/div>\n            \n            <div class=\"eael-tabs-content\">\n\t\t        \n                    <div id=\"home-tab\" class=\"clearfix eael-tab-content-item active-default\" data-title-link=\"home-tab\">\n\t\t\t\t        \t\t\t\t\t        <p><style type=\"text\/css\">\n.\u3002 {<br \/>\tfont-size: 18px;<br \/>}<br \/>.\u3002 {<br \/>\tfont-size: 18px;<br \/>}<br \/>.\u3002 {<br \/>\tfont-size: 18px;<br \/>}<br \/><\/style><\/p><p style=\"margin-bottom: 10px; line-height: 28px; color: #101010; font-family: \u5fae\u8edf\u6b63\u9ed1\u9ad4, Arial; font-size: 16px;\">\u672c\u8ab2\u7a0b\u662f\u7531\u00a0<a href=\"https:\/\/ece.nycu.edu.tw\/\" target=\"_blank\" rel=\"noopener\">\u570b\u7acb\u967d\u660e\u4ea4\u901a\u5927\u5b78\u96fb\u6a5f\u5de5\u7a0b\u5b78\u7cfb <\/a>\u63d0\u4f9b\u3002<\/p><p class=\"text\">The purpose of this course is to present a concise, yet mathematically rigorous, introduction to the main pillars of information theory. It thus naturally focuses on the foundational concepts and indispensable results of the subject for single-user systems, where a single data source or message needs to be reliably processed and communicated over a noiseless or noisy point-to-point channel.<br \/><br \/><\/p><p class=\"text\">At the first part of this course, six meticulously core chapters with accompanying problems, emphasizing the key topics of information measures, lossless and lossy data compression, channel coding, and joint source-channel coding. Two appendices covering necessary and supplementary material in real analysis and in probability and stochastic processes are included.<br \/><br \/><\/p><p class=\"text\">At the second part of the course, advanced topics concerning the information theoretic limits of discrete-time single-user stochastic systems with arbitrary statistical memory (i.e., systems that are not necessarily stationary, ergodic or information stable) will be covered.<br \/><br \/><\/p><p><span style=\"color: #3366ff; font-size: 16px;\">Textbook\uff1a<\/span><br \/>Fady Alajaji and Po-Ning Chen, An Introduction to Single-User Information Theory, Springer, July 2018.<\/p><p style=\"color: #ff00ff; font-size: 14px;\">For perfect learning results, please buy textbooks!<br \/><br \/><\/p><table style=\"color: #101010; font-family: \u5fae\u8edf\u6b63\u9ed1\u9ad4, Arial; font-size: 16px;\" border=\"1\"><tbody><tr><th style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\" width=\"100\">Instructor(s)<\/th><td bgcolor=\"#EEEEEE\" width=\"518\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a0College of Electrical and Computer Engineering Prof. Po-Ning Chen <\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/td><\/tr><tr><th style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\">Course Credits<\/th><td class=\"text\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a03\u00a0Credits<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/td><\/tr><tr><th class=\"text\" style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\">Academic Year<\/th><td class=\"text\" bgcolor=\"#EEEEEE\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a0107 Academic Year<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/td><\/tr><tr><th style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\">Level<\/th><td class=\"text\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a0Graduate Student<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/td><\/tr><tr><th style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\">Prior Knowledge<\/th><td class=\"text\" bgcolor=\"#EEEEEE\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a0A basic understanding of probability, real analysis and stochastic processes shall be of help for the study of this course.<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/td><\/tr><tr><th style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\">Related Resources<\/th><td class=\"text\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a0<a href=\"#video\">Course Video<\/a>\u00a0 \u00a0<a href=\"#syllabus\">Course Syllabus<\/a>\u00a0 \u00a0<a href=\"#calendar\">Course Calendar<\/a>\u00a0 \u00a0<a href=\"#handout\">Course Handout<\/a><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/td><\/tr><\/tbody><\/table>\t\t\t\t                            <\/div>\n\t\t        \n                    <div id=\"video-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"video-tab\">\n\t\t\t\t        \t\t\t\t\t        <p>\n<table id=\"tablepress-247\" class=\"tablepress tablepress-id-247\">\n<thead>\n<tr class=\"row-1 odd\">\n\t<th class=\"column-1\">Week<\/th><th class=\"column-2\">Course Content<\/th><th class=\"column-3\">Course Video<\/th>\n<\/tr>\n<\/thead>\n<tbody class=\"row-hover\">\n<tr class=\"row-2 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Overview: The philosophy behind information theory<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=70724\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-3 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 1:Introduction<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76411\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-4 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Appendix A:Overview on Suprema and Limits<br \/>\nA.1 Supremum and maximum<br \/>\nA.2 Infimum and minimum<br \/>\nA.3 Boundedness and suprema operations<br \/>\nA.4 Sequences and their limits<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76417\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-5 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Appendix A:Overview on Suprema and Limits<br \/>\nReview A.1-A.4<br \/>\nA.5 Equivalence<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76423\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-6 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Appendix B:Overview in Probability and Random Processes<br \/>\nB.1 Probability space<br \/>\nB.2 Random variables and random processes<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76429\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-7 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Appendix B:Overview in Probability and Random Processes<br \/>\nB.3 Statistical properties of random sources<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76435\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-8 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Appendix B:Overview in Probability and Random<br \/>\nProcesses<br \/>\nB.5 Ergodicity and law of large numbers<br \/>\nB.6 Central limit theorem<br \/>\nB.7 Convexity, concavity and Jensen\u2019s inequality<br \/>\nB.8 Lagrange multipliers tech. &amp; KKT conditions<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76441\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-9 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 2\uff1aInformation Measures for Discrete Systems<br \/>\n2.1.1 Self-information<br \/>\n2.1.2 Entropy<br \/>\n2.1.3 Properties of entropy<br \/>\n2.1.4 Joint entropy and conditional entropy<br \/>\n2.1.5 Properties of joint and conditional entropy<br \/>\n2.2 Mutual information<br \/>\n2.2.1 Properties of m<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76447\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-10 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 2\uff1aInformation Measures for Discrete Systems<br \/>\n2.4 Data processing inequality<br \/>\n2.5 Fano\u2019s inequality<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76453\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-11 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 2\uff1aInformation Measures for Discrete Systems<br \/>\n2.6 Divergence and variational distance<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76459\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-12 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 2\uff1aInformation Measures for Discrete System<br \/>\n2.7 Convexity\/concavity of information measures<br \/>\n2.8 Fundamentals of hypothesis testing<br \/>\n2.9 R\u00b4enyi\u2019s information measures<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76465\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-13 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 3\uff1aLossless Data Compression<br \/>\n3.1 Principles of data compression<br \/>\n3.2.1 Block codes for DMS<br \/>\n3.2.2 Block Codes for Stationary Ergodic Sources<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76471\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-14 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 3\uff1aLossless Data Compression<br \/>\n3.3 Variable-Length Code for Lossless Data Comp.<br \/>\n3.3.1 Non-singular Codes and Uniquely Decodable Codes<br \/>\n3.3.2 Prefix or Instantaneous Codes<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76477\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-15 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 3\uff1aLossless Data Compression<br \/>\n3.3.3 Examples of Binary Prefix Codes<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76483\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-16 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 3\uff1aLossless Data Compression<br \/>\n3.3.4 Universal Lossless Variable-Length Codes<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76489\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-17 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 4:Data Transmission and Channel Capacity<br \/>\n4.3 Block codes for data transmission over DMCs(1\/3)<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76495\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-18 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 4:Data Transmission and Channel Capacity<br \/>\n4.3 Block codes for data transmission over DMCs(2\/3)<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76501\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-19 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 4:Data Transmission and Channel Capacity<br \/>\n4.3 Block codes for data transmission over DMCs(3\/3)<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76507\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-20 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 4:Data Transmission and Channel Capacity<br \/>\n4.5 Calculating channel capacity<br \/>\n4.5.1 Symmetric, Weakly Symmetric, and Quasi-symmetric Channels<br \/>\n4.5.2 Karuch-Kuhn-Tucker cond. for chan. capacity<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76513\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-21 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 4:Data Transmission and Channel Capacity<br \/>\n4.4 Example of Polar Codes for the BEC<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76519\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-22 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 4:Data Transmission and Channel Capacity<br \/>\n4.6 Lossless joint source-channel coding and Shannon\u2019s separation principle<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76525\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-23 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 5\uff1aDifferential Entropy and Gaussian Channels<br \/>\n5.1 Differential entropy<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76531\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-24 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 5\uff1aDifferential Entropy and Gaussian Channels<br \/>\n5.1 Differential entropy<br \/>\n5.2 Joint &amp; cond. diff. entrop., diverg. &amp; mutual info<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76537\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-25 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 5\uff1aDifferential Entropy and Gaussian Channels<br \/>\n5.3 AEP for continuous memoryless sources<br \/>\n5.4 Capacity for discrete memoryless Gaussian chan<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76543\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-26 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 5\uff1aDifferential Entropy and Gaussian Channels<br \/>\n5.5 Capacity of Uncorrelated Parallel Gaussian Chan<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76549\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-27 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 5\uff1aDifferential Entropy and Gaussian Channels<br \/>\n5.6 Capacity of correlated parallel Gaussian channels<br \/>\n5.7 Non-Gaussian discrete-time memoryless channels<br \/>\n5.8 Capacity of band-limited white Gaussian channel<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76555\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-28 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 6\uff1aLossy Data Compression and Transmission<br \/>\n6.1.1 Motivation<br \/>\n6.1.2 Distortion measures<br \/>\n6.1.3 Frequently used distortion measures<br \/>\n6.2 Fixed-length lossy data compression<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76561\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-29 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 6\uff1aLossy Data Compression and Transmission<br \/>\n6.3 Rate-distortion theorem<br \/>\nAEP for distortion typical set<br \/>\nShannon\u2019s lossy source coding theorem<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76567\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-30 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 6\uff1aLossy Data Compression and Transmission<br \/>\n6.4 Calculation of the rate-distortion function<br \/>\n6.4.2 Rate distortion func \/ the squared error dist<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76573\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-31 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 6\uff1aLossy Data Compression and Transmission<br \/>\n6.5 Lossy joint source-channel coding theorem<br \/>\n6.6 Shannon limit of communication systems(1\/2)<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76579\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-32 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 6\uff1aLossy Data Compression and Transmission<br \/>\n6.6 Shannon limit of communication systems(2\/2)<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76585\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-33 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Preface and Introduction<br \/>\nChapter 1:Generalized Information Measures for Arbitrary Systems with Memory<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76591\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-34 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 2:General Data Compression Theorems<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76597\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-35 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 3:Measure of Randomness for Stochastic Processes<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76603\">Watch Online<\/a><\/td>\n<\/tr>\n<tr class=\"row-36 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Chapter 4:Channel Coding Theorems and Approximations of Output Statistics for Arbitrary Channels<\/td><td class=\"column-3\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&amp;p=76609\">Watch Online<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<!-- #tablepress-247 from cache --><\/p>\t\t\t\t                            <\/div>\n\t\t        \n                    <div id=\"syllabus-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"syllabus-tab\">\n\t\t\t\t        \t\t\t\t\t        <p class=\"title\"><strong>\u8ab2\u7a0b\u76ee\u6a19<\/strong><\/p><p class=\"text\">The purpose of this course is to present a concise, yet mathematically rigorous, introduction to the main pillars of information theory. It thus naturally focuses on the foundational concepts and indispensable results of the subject for single-user systems, where a single data source or message needs to be reliably processed and communicated over a noiseless or noisy point-to-point channel.<br \/><br \/><\/p><p class=\"text\">At the first part of this course, six meticulously core chapters with accompanying problems, emphasizing the key topics of information measures, lossless and lossy data compression, channel coding, and joint source-channel coding. Two appendices covering necessary and supplementary material in real analysis and in probability and stochastic processes are included.<br \/><br \/><\/p><p class=\"text\">At the second part of the course, advanced topics concerning the information theoretic limits of discrete-time single-user stochastic systems with arbitrary statistical memory (i.e., systems that are not necessarily stationary, ergodic or information stable) will be covered.<\/p><p>\u00a0<\/p><p class=\"title\"><strong>\u8ab2\u7a0b\u66f8\u76ee<\/strong><\/p><p class=\"text\">Fady Alajaji and Po-Ning Chen, An Introduction to Single-User Information Theory, Springer, July 2018.<\/p><p class=\"text\">The following is a list of recommended references:<br \/>1. A Student\u2019s Guide to Coding and Information Theory, Stefan M. Moser and Po-Ning Chen, Cambridge University Press, January 2012.<br \/>2. Elements of Information Theory, Thomas M. Cover and Joy A. Thomas, 2nd edition, John Wiley &amp; Sons, Inc., July 2006.<br \/>3. A First Course in Information Theory (Information Technology: Transmission, Processing, and Storage), Raymond W. Yeung, Plenum Pub Corp., May 2002.<br \/>4. Principles and Practices of Information Theory, Richard E. Blahut, Addison Wesley, 1988.<br \/>5. Information Theory and Reliable Communication, Robert G. Gallager, 1985.<br \/>6. Information Theory, Robert B. Ash, Dover Publications, Inc., 1965.<br \/>7. Mathematical Foundations of Information Theory, A. I. Khinchin, Dover Publications, Inc., 1957.<\/p><p>\u00a0<\/p><p class=\"title\"><strong>\u8a55\u5206\u6a19\u6e96<\/strong><\/p><table border=\"1\" cellspacing=\"0\"><tbody><tr class=\"stitle\"><td bgcolor=\"#e4d8b0\"><strong>\u9805\u76ee<\/strong><\/td><td bgcolor=\"#e4d8b0\"><strong>\u767e\u5206\u6bd4<\/strong><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">\u671f\u4e2d\u8003<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">50%<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">\u671f\u672b\u8003<\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">50%<\/span><\/td><\/tr><\/tbody><\/table>\t\t\t\t                            <\/div>\n\t\t        \n                    <div id=\"calendar-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"calendar-tab\">\n\t\t\t\t        \t\t\t\t\t        <p><span style=\"font-family: \u65b0\u7d30\u660e\u9ad4; font-size: 15px;\">\u672c\u8ab2\u7a0b\u884c\u4e8b\u66c6\u63d0\u4f9b\u8ab2\u7a0b\u9032\u5ea6\u8207\u8003\u8a66\u8cc7\u8a0a\u53c3\u8003\u3002<\/span><\/p><table style=\"font-style: normal; line-height: 20px; font-size: 15px; text-align: center;\" border=\"1\" cellspacing=\"0\"><tbody><tr><td bgcolor=\"#e4d8b0\" width=\"76\"><div style=\"text-align: center;\"><span style=\"color: #000000; font-weight: bold;\"><strong>\u5b78\u671f\u9031\u6b21<\/strong><\/span><\/div><\/td><td bgcolor=\"#e4d8b0\" width=\"135\"><div style=\"text-align: center;\" align=\"center\"><span style=\"font-family: 'Times New Roman'; font-weight: bold;\"><strong>\u4e0a\u8ab2\u65e5\u671f<\/strong><\/span><\/div><\/td><td bgcolor=\"#e4d8b0\" width=\"440\"><div style=\"text-align: center; font-size: 15px; font-weight: bold;\"><strong>\u53c3\u8003\u8ab2\u7a0b\u9032\u5ea6<\/strong><\/div><\/td><\/tr><tr><td style=\"text-align: center;\"><p><span class=\"text\">\u7b2c\u4e00\u9031<\/span><\/p><\/td><td style=\"line-height: 20px; font-style: normal; font-size: 15px; text-align: center;\">2019\/02\/22<\/td><td style=\"text-align: left; padding: 5px;\"><ul><li>A coherent introduction of the primary principles of single-user information theory<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\"><span class=\"text\">\u7b2c\u4e8c\u9031<\/span><\/td><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\">2019\/03\/01<\/td><td style=\"text-align: left; padding: 5px;\" bgcolor=\"#f2f2ec\"><ul><li>Holiday<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\"><span class=\"text\">\u7b2c\u4e09\u9031<\/span><\/td><td style=\"text-align: center;\">2019\/03\/08<\/td><td style=\"text-align: left; padding: 5px;\"><ul><li>Overview in the subjects of suprema, limits, probability and random processes (e.g., random variables, statistical properties of random processes, Markov chains, convergence of sequences of random variables, ergodicity and laws of large numbers, central limit theorem, concavity and convexity, Jensen\u2019s inequality, Lagrange multipliers, and the Karush\u2013Kuhn\u2013Tucker (KKT) conditions for constrained optimization problems)\u3000<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\"><span class=\"text\">\u7b2c\u56db\u9031<\/span><\/td><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\">2019\/03\/15<\/td><td style=\"text-align: left; padding: 5px;\" bgcolor=\"#f2f2ec\"><ul><li>Information measures for discrete systems and their properties (self-information, entropy, mutual information and divergence, data processing theorem, Fano\u2019s inequality, Pinsker\u2019s inequality, simple hypothesis testing, Neyman\u2013Pearson lemma, Chernoff\u2013Stein lemma, and Re\u0301nyi\u2019s information measures)\u3000<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\"><span class=\"text\">\u7b2c\u4e94\u9031<\/span><\/td><td style=\"text-align: center;\">2019\/03\/22<\/td><td style=\"text-align: left; padding: 5px;\"><ul><li>Fundamentals of lossless source coding (i.e., data compression): discrete memoryless sources, fixed-length (block) codes for asymptotically lossless compression, AEP, fixed-length source coding theorems for memoryless and stationary ergodic sources, entropy rate and redundancy, variable-length codes for lossless compression, variable-length source coding theorems for memoryless and stationary sources, prefix codes, Kraft inequality, Huffman codes, Shannon\u2013Fano\u2013Elias codes, and Lempel\u2013Ziv codes\u3000<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\"><span class=\"text\">\u7b2c\u516d\u9031<\/span><\/td><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\">2019\/03\/29<\/td><td style=\"text-align: left; padding: 5px;\" bgcolor=\"#f2f2ec\"><ul><li>Fundamentals of lossless source coding (i.e., data compression): discrete memoryless sources, fixed-length (block) codes for asymptotically lossless compression, AEP, fixed-length source coding theorems for memoryless and stationary ergodic sources, entropy rate and redundancy, variable-length codes for lossless compression, variable-length source coding theorems for memoryless and stationary sources, prefix codes, Kraft inequality, Huffman codes, Shannon\u2013Fano\u2013Elias codes, and Lempel\u2013Ziv codes<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\"><span class=\"text\">\u7b2c\u4e03\u9031<\/span><\/td><td style=\"text-align: center;\">2019\/04\/05<\/td><td style=\"text-align: left; padding: 5px;\"><ul><li>Holiday<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\"><span class=\"text\">\u7b2c\u516b\u9031<\/span><\/td><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\">2019\/04\/12<\/td><td style=\"text-align: left; padding: 5px;\" bgcolor=\"#f2f2ec\"><ul><li>Fundamentals of channel coding: discrete memoryless channels, block codes for data transmission, channel capacity, coding theorem for discrete memoryless channels, calculation of channel capacity, channels with symmetric structures, lossless joint source\u2013channel coding, and Shannon\u2019s separation principle<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\"><span class=\"text\">\u7b2c\u4e5d\u9031<\/span><\/td><td style=\"text-align: center;\">2019\/04\/19<\/td><td style=\"text-align: left; padding: 5px;\"><ul><li>Fundamentals of channel coding: discrete memoryless channels, block codes for data transmission, channel capacity, coding theorem for discrete memoryless channels, calculation of channel capacity, channels with symmetric structures, lossless joint source\u2013channel coding, and Shannon\u2019s separation principle<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\"><span class=\"text\">\u7b2c\u5341\u9031<\/span><\/td><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\">2019\/04\/26<\/td><td style=\"text-align: left; padding: 5px;\" bgcolor=\"#F2F2EC\"><ul><li>Midterm Exam<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\"><span class=\"text\">\u7b2c\u5341\u4e00\u9031<\/span><\/td><td style=\"text-align: center;\">2019\/05\/03<\/td><td style=\"text-align: left; padding: 5px;\"><ul><li>Information measures for continuous alphabet systems and Gaussian channels: differential entropy, mutual information and divergence, AEP for continuous memoryless sources, capacity and channel coding theorem of discrete-time memoryless Gaussian channels, capacity of uncorrelated parallel Gaussian channels and the water-filling principle, capacity of correlated Gaussian channels, non-Gaussian discrete-time memoryless channels, and capacity of band-limited (continuous-time) white Gaussian channels<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\"><span class=\"text\">\u7b2c\u5341\u4e8c\u9031<\/span><\/td><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\">2019\/05\/10<\/td><td style=\"text-align: left; padding: 5px;\" bgcolor=\"#f2f2ec\"><ul><li>Fundamentals of lossy source coding and joint source\u2013channel coding: distortion measures, rate\u2013distortion theorem for memoryless sources, rate\u2013distortion theorem for stationary ergodic sources, rate\u2013distortion function and its properties, rate\u2013distortion function for memoryless Gaussian sources, lossy joint source\u2013channel coding theorem, and Shannon limit of communication systems<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\"><span class=\"text\">\u7b2c\u5341\u4e09\u9031<\/span><\/td><td style=\"text-align: center;\">2019\/05\/17<\/td><td style=\"text-align: left; padding: 5px;\"><ul><li>Fundamentals of lossy source coding and joint source\u2013channel coding: distortion measures, rate\u2013distortion theorem for memoryless sources, rate\u2013distortion theorem for stationary ergodic sources, rate\u2013distortion function and its properties, rate\u2013distortion function for memoryless Gaussian sources, lossy joint source\u2013channel coding theorem, and Shannon limit of communication systems<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\"><span class=\"text\">\u7b2c\u5341\u56db\u9031<\/span><\/td><td style=\"line-height: 20px; font-style: normal; font-size: 15px; text-align: center;\" bgcolor=\"#f2f2ec\">2019\/05\/24<\/td><td style=\"text-align: left; padding: 5px;\" bgcolor=\"#f2f2ec\"><ul><li>General information measure: Information spectrum and Quantile and their properties<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\"><span class=\"text\">\u7b2c\u5341\u4e94\u9031<\/span><\/td><td style=\"line-height: 20px; font-style: normal; font-size: 15px; text-align: center;\">2019\/05\/31<\/td><td style=\"text-align: left; padding: 5px;\"><ul><li>Advanced topics of losslesss data compression: Fixed-length lossless data compression theorem for arbitrary channels, variable-length lossless data compression theorem for arbitrary channels<\/li><\/ul><\/td><\/tr><tr><td style=\"text-align: center;\" bgcolor=\"#f2f2ec\"><span class=\"text\">\u7b2c\u5341\u516d\u9031<\/span><\/td><td style=\"line-height: 20px; font-style: normal; font-size: 15px; text-align: center;\" bgcolor=\"#f2f2ec\">2019\/06\/07<\/td><td style=\"text-align: left; padding: 5px;\" bgcolor=\"#f2f2ec\"><ul><li>Holiday<\/li><\/ul><\/td><\/tr><\/tbody><\/table>\t\t\t\t                            <\/div>\n\t\t        \n                    <div id=\"handout-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"handout-tab\">\n\t\t\t\t        \t\t\t\t\t        <p><strong class=\"title\">Laboratory Manuals<\/strong><\/p><table style=\"border-collapse; line-height: 17px; font-size: 15px;\" border=\"1\"><tbody><tr><td bgcolor=\"#e4d8b0\" width=\"280\"><div class=\"stitle\" style=\"text-align: center;\" align=\"center\"><span style=\"font-family: 'Times New Roman';\"><strong>\u7ae0\u7bc0<\/strong><\/span><\/div><\/td><td bgcolor=\"#e4d8b0\" width=\"150\"><div class=\"stitle\" style=\"text-align: center;\" align=\"center\"><span style=\"font-family: 'Times New Roman';\"><strong>\u4e0b\u8f09\u9023\u63a5<\/strong><\/span><\/div><\/td><\/tr><tr style=\"height: 0px;\"><td style=\"text-align: left; padding: 5px;\">Overview : The philosophy behind information theory Chapter 1 Introduction<\/td><td style=\"width: 30%; height: 0px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/Overview%20%26%20Chapter%201.pdf\">PDF<\/a><\/td><\/tr><tr style=\"height: 0px;\" bgcolor=\"#f2f2ec\"><td style=\"text-align: left; padding: 5px;\">Appendix A Overview on Suprema and Limits<\/td><td style=\"width: 30%; height: 0px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/AppendixA.pdf\">PDF<\/a><\/td><\/tr><tr style=\"height: 21px;\"><td style=\"text-align: left; padding: 5px;\">Appendix B Overview in Probability and Random Processes<\/td><td style=\"width: 30%; height: 21px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/AppendixB.pdf\">PDF<\/a><\/td><\/tr><tr style=\"height: 21px;\" bgcolor=\"#f2f2ec\"><td style=\"text-align: left; padding: 5px;\">Chapter 2 Information Measures for Discrete Systems<\/td><td style=\"width: 30%; height: 21px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/Chapter%202.pdf\">PDF<\/a><\/td><\/tr><tr style=\"height: 21px;\"><td style=\"text-align: left; padding: 5px;\">Chapter 3 Lossless Data Compression<\/td><td style=\"width: 30%; height: 21px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/Chapter%203.pdf\">PDF<\/a><\/td><\/tr><tr style=\"height: 21px;\" bgcolor=\"#f2f2ec\"><td style=\"text-align: left; padding: 5px;\">Chapter 4 Data Transmission and Channel Capacity<\/td><td style=\"width: 30%; height: 21px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/Chapter%204.pdf\">PDF<\/a><\/td><\/tr><tr style=\"height: 21px;\"><td style=\"text-align: left; padding: 5px;\">Chapter 5 Differential Entropy and Gaussian Channels<\/td><td style=\"width: 30%; height: 21px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/Chapter%205.pdf\">PDF<\/a><\/td><\/tr><tr style=\"height: 21px;\" bgcolor=\"#f2f2ec\"><td style=\"text-align: left; padding: 5px;\">Chapter 6 Lossy Data Compression and Transmission<\/td><td style=\"width: 30%; height: 21px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/Chapter%206.pdf\">PDF<\/a><\/td><\/tr><tr style=\"height: 21px;\"><td style=\"text-align: left; padding: 5px;\">Preface and Introduction<\/td><td style=\"width: 30%; height: 21px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/Preface%20and%20Introduction.pdf\">PDF<\/a><\/td><\/tr><tr style=\"height: 21px;\" bgcolor=\"#f2f2ec\"><td style=\"text-align: left; padding: 5px;\">Chapter 1 Generalized Information Measures for Arbitrary Systems with Memory<\/td><td style=\"width: 30%; height: 21px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/Generalized%20Information%20Measures%20for%20Arbitrary%20Systems%20with%20Memory.pdf\"><span style=\"text-align: center;\">PDF<\/span><\/a><\/td><\/tr><tr style=\"height: 21px;\"><td style=\"text-align: left; padding: 5px;\">Chapter 2 General Data Compression Theorems<\/td><td style=\"width: 30%; height: 21px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/General%20Data%20Compression%20Theorems.pdf\"><span style=\"text-align: center;\">PDF<\/span><\/a><\/td><\/tr><tr style=\"height: 21px;\" bgcolor=\"#f2f2ec\"><td style=\"text-align: left; padding: 5px;\">Chapter 3 Measure of Randomness for Stochastic Processes<\/td><td style=\"width: 30%; height: 21px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/Measure%20of%20Randomness%20for%20Stochastic%20Processes.pdf\"><span style=\"text-align: center;\">PDF<\/span><\/a><\/td><\/tr><tr style=\"height: 21px;\"><td style=\"text-align: left; padding: 5px;\">Chapter 4 Channel Coding Theorems and<br \/>Approximations of Output Statistics for<br \/>Arbitrary Channels<\/td><td style=\"width: 30%; height: 21px; text-align: center;\"><a href=\"https:\/\/video.ocw.nycu.edu.tw\/pub\/inft072\/handout\/Channel%20Coding%20Theorems%20and%20Approximations%20of%20Output%20Statistics%20for%20Arbitrary%20Channels.pdf\"><span style=\"text-align: center;\">PDF<\/span><\/a><\/td><\/tr><\/tbody><\/table>\t\t\t\t                            <\/div>\n\t\t                    <\/div>\n        <\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d7723a8 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" 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href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/school-of-law\" class=\"elementor-item\" tabindex=\"-1\">School of Law<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-118069\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/school-of-nursing-2\" class=\"elementor-item\" tabindex=\"-1\">School of Nursing<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-118071\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/college-of-photonics\" class=\"elementor-item\" tabindex=\"-1\">College of Photonics<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-125874\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/college-of-biomedical-science-and-engineering\" class=\"elementor-item\" tabindex=\"-1\">College of Biomedical Science and Engineering<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-132738\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/college-of-life-sciences-en\" class=\"elementor-item\" tabindex=\"-1\">College of Life Sciences<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-141797\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/colege-of-medicine-en\" class=\"elementor-item\" tabindex=\"-1\">College of Medicine<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-118072\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/course-of-chinese-center\" class=\"elementor-item\" tabindex=\"-1\">Course of Chinese Center<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-120431\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/liberal-arts-college\" class=\"elementor-item\" tabindex=\"-1\">Forum Of Liberal Arts<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-123746\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/speech-of-general-education-center\" class=\"elementor-item\" tabindex=\"-1\">Contemporary Civilization<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-128240\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/reading-of-classic-masterpiece\" class=\"elementor-item\" tabindex=\"-1\">Reading of Classic Masterpiece<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-123745\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course-2\/course-of-general-education-center-2\" class=\"elementor-item\" tabindex=\"-1\">Course of General Education Center<\/a><\/li>\n<\/ul>\t\t\t<\/nav>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Information Th&hellip;<\/p>\n","protected":false},"featured_media":70662,"parent":5301,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"footnotes":""},"categories":[4321],"tags":[341,4501],"class_list":["post-70712","course_page","type-course_page","status-publish","format-standard","has-post-thumbnail","hentry","category-information-theory-107-en","tag-label-all-course-en","tag-label-college-of-electrical-and-computer-engineering-en"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Information Theory \u2013 107 Academic Year | College of Electrical and Computer Engineering Prof. Po-Ning Chen - NYCU 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