{"id":5112,"date":"2023-05-20T16:02:42","date_gmt":"2023-05-20T08:02:42","guid":{"rendered":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5112"},"modified":"2023-09-23T00:39:15","modified_gmt":"2023-09-22T16:39:15","slug":"%e5%a4%a7%e6%95%b8%e6%93%9a%e3%80%81%e6%a9%9f%e5%99%a8%e5%ad%b8%e7%bf%92%e8%88%87%e4%ba%ba%e5%b7%a5%e6%99%ba%e6%85%a7112-1","status":"publish","type":"page","link":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5112","title":{"rendered":"\u5927\u6578\u64da\u3001\u6a5f\u5668\u5b78\u7fd2\u8207\u4eba\u5de5\u667a\u6167(112-1)"},"content":{"rendered":"<h4><span style=\"color: #008000; font-family: 'times new roman', times, serif; font-size: 14pt;\"><strong>\u8ab2\u7a0b\u5927\u7db1<\/strong><\/span><\/h4>\n<p><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">Prerequisite:<\/span><\/p>\n<ul>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">Programming Proficiency: It is essential for students to have familiarity with at least one high-level programming language.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">Expertise in scientific programming languages, such as R, Matlab, Python, Julia, or SAS, will be especially beneficial.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">Relational Databases SQL: Students must possess a foundational understanding of relational databases and the Structured Query<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">Language (SQL). This knowledge is pivotal for many data engineering components within the course.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">Data Structures Fundamentals: A grasp of basic data structures, including but not limited to arrays, lists, sets, and dictionaries, is vital. This understanding will serve students well as they delve into the intricacies of Data Engineering and Analytics.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">Introductory Statistics: A solid grounding in statistical concepts is required. This includes understanding descriptive analytics,<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">which encompasses measures of data dispersion and central tendency, as well as diagnostic analytics, such as hypothesis testing. Such<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">knowledge will pave the way for a deeper comprehension of data analytics methodologies and statistical machine learning algorithms.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">Analytical Problem-solving Skills: The practical aspects of this course necessitate strong analytical thinking and<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">problem-solving abilities. Students should be prepared to apply their knowledge to tackle real-world challenges.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">For those students who may find themselves lacking in any of the outlined prerequisites, it is strongly advised to pursue supplementary <\/span><br \/>\n<span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">coursework or dedicated self-study. This proactive approach will ensure a richer and more effective learning experience throughout the course.<\/span><\/p>\n<h4><span style=\"color: #008000; font-family: 'times new roman', times, serif; font-size: 14pt;\"><strong>\u8ab2\u7a0b\u76ee\u6a19<\/strong><\/span><\/h4>\n<p><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">This course offers an in-depth exploration of the multifaceted domains of AI, Machine Learning (ML), and Big Data Analytics. Students will gain a foundational understanding of Data Science, tracing its evolution and significance across diverse sectors such as business, healthcare, insurance, and finance.<br \/>\nThe curriculum addresses essential topics, encompassing Big Data Analytics, Data Engineering, Business Analytics, Machine Learning Design Patterns, and the nuances between Supervised &amp; Unsupervised Learning. Enhancing the course content, students will explore foundational concepts like deep neural networks, model architecture design, functional programming, and ML Interpretability, which aim to demystify the black-box nature of many ML models. A central component of our curriculum is the transformative potential of large language models, such as the GPT-series. Students will appreciate how these models are pivotal in data analytics, especially in their capacity to generate R and Python code for streamlined and automated data processing.<br \/>\nThroughout the course, the emphasis on practical applications ensures that students garner hands-on experience with the R and Python programming languages, addressing modern data analytics challenges. This course is tailored for those eager to both grasp and apply the principles of data science and ML\/AI in concrete real-world contexts.<\/span><\/p>\n<h4><span style=\"color: #008000; font-family: 'times new roman', times, serif; font-size: 14pt;\"><strong>\u6388\u8ab2\u65b9\u5f0f<\/strong><\/span><\/h4>\n<ol>\n<li style=\"font-family: 'times new roman', times, serif; font-size: 14pt; margin-bottom: 10px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Onsite &amp; online video lectures<\/span><\/li>\n<li style=\"font-family: 'times new roman', times, serif; font-size: 14pt; margin-bottom: 10px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">In-class quiz<\/span><\/li>\n<\/ol>\n<h4><span style=\"color: #008000; font-family: 'times new roman', times, serif; font-size: 14pt;\"><strong>\u8a55\u5206\u65b9\u5f0f<\/strong><\/span><\/h4>\n<ol>\n<li style=\"font-family: 'times new roman', times, serif; font-size: 14pt; margin-bottom: 10px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Quiz\uff1a10%<\/span><\/li>\n<li style=\"font-family: 'times new roman', times, serif; font-size: 14pt; margin-bottom: 10px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">In-class exercise\uff1a20%<\/span><\/li>\n<li style=\"font-family: 'times new roman', times, serif; font-size: 14pt; margin-bottom: 10px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Homework\uff1a30%<\/span><\/li>\n<li style=\"font-family: 'times new roman', times, serif; font-size: 14pt; margin-bottom: 10px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Midterm Proposal\uff1a20%<\/span><\/li>\n<li><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Final Project\uff1a20%<\/span><\/li>\n<\/ol>\n<h4><span style=\"color: #008000; font-family: 'times new roman', times, serif; font-size: 14pt;\"><strong>\u53c3\u8003\u66f8\/\u6559\u79d1\u66f8\/\u95b1\u8b80\u6587\u737b<\/strong><\/span><\/h4>\n<ul>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">G. James, D. Witten, T. Hastie, and R. Tibshirani,<em> An Introduction to Statistical Learning: with Applications in R<\/em>, 2nd Edition, Springer (Available free online: <a href=\"https:\/\/www.statlearning.com\/\">https:\/\/www.statlearning.com\/<\/a>)<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">G. James, D. Witten, T. Hastie, and R. Tibshirani, J. Taylor, <em>An Introduction to Statistical Learning: with Applications in Python<\/em>, Springer (Available free online:<a href=\"https:\/\/www.statlearning.com\/\"> https:\/\/www.statlearning.com\/<\/a>)<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">F. Buisson,<em> Behavioral Data Analysis with R and Python<\/em>,\u00a0O\u2019Reilly Media, Inc., 2021.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">K. Hwang and M. Chen, <em>Big-Data Analytics for Cloud, IoT and Cognitive Computing<\/em>, 1st ed. Wiley Publishing, 2017.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">EMC Education Services,<em> Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data<\/em>, John Wiley &amp; Sons, 2015.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">N. Matloff, <em>The Art of R Programming: A Tour of Statistical Software Design<\/em>, 1st edition. No Starch Press, 2011.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">Kabacoff, Robert, <em>R in Action<\/em>, Manning Publications Co., 2011<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">C. O&#8217;Neil and R. Schutt, <em>Doing Data Science: Straight Talk from the Frontline<\/em>, 1st edition. O&#8217;Reilly Media, 2013.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">F. Chollet and J. J. Allaire, <em>Deep Learning with R<\/em>, 1 edition. Manning Publications, 2018.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">A. G\u00e9ron, <em>Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems<\/em>, O\u2019Reilly Media, Inc., 2019.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">W. McKinney, <em>Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython<\/em>,\u00a0O\u2019Reilly Media, Inc., 2017.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">F. Provost, T. Fawcett,<em> Data Science for Business: What you need to know about data mining and data-analytic thinking<\/em>, O&#8217;Reilly Media, Inc., 2013.<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">C. Molnar, <em>Interpretable Machine Learning: A Guide For Making Black Box Models Explainable<\/em>. Munich, Germany: Independently published, 2022. (<a href=\"https:\/\/christophm.github.io\/interpretable-ml-book\/\">https:\/\/christophm.github.io\/interpretable-ml-book\/<\/a>)<\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'times new roman', times, serif;\">J. Pearl, M. Glymour, and N. P. Jewell, <em>Causal Inference in Statistics &#8211; A Primer<\/em>, 1st edition. Chichester, West Sussex: Wiley, 2016.<\/span><\/li>\n<\/ul>\n<h4><span style=\"color: #008000; font-family: 'times new roman', times, serif; font-size: 14pt;\"><strong>\u8ab2\u7a0b\u5167\u5bb9\u53ca\u9032\u5ea6<\/strong><\/span><\/h4>\n<table style=\"max-width: 700px; width: 100%; overflow-x: auto; font-size: 14pt; height: auto;\">\n<tbody>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px; background-color: #acd4ff;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Week<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px; text-align: center; background-color: #acd4ff;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Syllabus<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">1<\/span><\/td>\n<td width=\"449\" style=\"width: 91%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Course Introduction<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">2<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Data Engineering \u2014 I<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">3<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Data Engineering \u2014 II<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">4<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Data Engineering \u2014 III<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td tyle=\"text-align: right; width: 9%;\" style=\"text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">5<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">\u00a0Fundamentals of Data Analytics \u2014 I<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">6<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Fundamentals of Data Analytics \u2014 II<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">7<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">\u00a0Fundamentals of Data Analytics \u2014 III<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">8<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Introduction to Statistical Learning \u2014 I<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">9<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Project Proposal Defense \u2013 I<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">10<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Project Proposal Defense \u2013 II<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">11<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">\u00a0Introduction to Statistical Learning \u2014 II<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">12<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">\u00a0Supervised Learning \u2014 Regression<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">13<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Supervised Learning \u2014 Classification<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">14<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Introduction to Unsupervised Learning<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">15<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Topics in Interpretable Machine Learning and Causal Inference<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">16<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Term Project Presentation \u2014 I<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">17<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Term Project Presentation \u2014 II<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">18<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u8ab2\u7a0b\u5927\u7db1 Prerequisite: Programming Proficiency: It is essential for students to have familiarity with at least one high-level programming language. Expertise in scientific programming languages, such as R, Matlab, Python, Julia, or SAS, will be especially beneficial. Relational Databases SQL: Students must possess a foundational understanding of relational databases and the Structured Query Language (SQL). This knowledge is pivotal for many data engineering components within the course. Data Structures Fundamentals: A grasp of basic data structures, including but not limited to arrays, lists, sets, and dictionaries, is vital. This understanding will serve students well as they delve into the intricacies of Data Engineering and Analytics. Introductory Statistics: A solid grounding in statistical concepts is required. This includes understanding descriptive analytics, which encompasses measures of data dispersion and central tendency, as well as diagnostic analytics, such as hypothesis testing. Such knowledge will pave the way for a deeper comprehension of data analytics methodologies and statistical machine learning algorithms. Analytical Problem-solving Skills: The practical aspects of this course necessitate strong analytical thinking and problem-solving abilities. Students should be prepared to apply their knowledge to tackle real-world challenges. For those students who may find themselves lacking in any of the outlined prerequisites, it is strongly advised to pursue supplementary coursework or dedicated self-study. This proactive approach will ensure a richer and more effective learning experience throughout the course. \u8ab2\u7a0b\u76ee\u6a19 This course offers an in-depth exploration of the multifaceted domains of AI, Machine Learning (ML), and Big Data Analytics. Students will gain a foundational understanding of Data Science, tracing its evolution and significance across diverse sectors such as business, healthcare, insurance, and finance. The curriculum addresses essential topics&#8230;<\/p>\n","protected":false},"author":69,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-5112","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/5112","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/users\/69"}],"replies":[{"embeddable":true,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5112"}],"version-history":[{"count":10,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/5112\/revisions"}],"predecessor-version":[{"id":5648,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/5112\/revisions\/5648"}],"wp:attachment":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5112"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}