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Course Description Course Name : Web Mining Course Code : CENG-564 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Spring (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. SELMA AYŞE ÖZEL Learning Outcomes of the Course : Learns the properties of the data on the Web. Learns and applies the techniques necessary for crawling, indexing, and querying Web pages. Applies supervised and unsupervised learning methods over the Web data. Extracts information from Web data. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : The aim of this course is to process data on the Web and to investigate the hidden information and relations from these data. Course Contents : Overview of data mining techniques: Association Rules and Sequential Patterns, Supervised Learning, Unsupervised Learning, Partially Supervised Learning. Information Retrieval and Web Search. Social Network Analysis. Web Crawling. Structured Data Extraction: Wrapper Generation. Information Integration. Opinion Mining and Sentiment Analysis. Web Usage Mining. Language of Instruction : English Work Place : Room   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Association Rules and Sequential Patterns. Reading the lecture notes Lecture, sample applications in class 2 Supervised Learning: Classification Techniques. Reading the lecture notes Lecture, sample applications in class 3 Unsupervised Learning: Clustering Techniques. Reading the lecture notes, making research for the presentation topic Lecture, sample applications in class 4 Partially Supervised Learning: Learning from Labeled and Unlabeled Examples, Learning from Positive and Unlabeled Examples. Reading the lecture notes, making research for the presentation topic Lecture, sample applications in class 5 Information Retrieval and Web Search. Reading the lecture notes, making research for the presentation topic Lecture, sample applications in class 6 Social Network Analysis Reading the lecture notes, preparing the oral presentation. Lecture, sample applications in class 7 Web Crawling Reading the lecture notes, preparing the oral presentation. Lecture, sample applications in class 8 Structured Data Extraction: Wrapper Generation Reading the lecture notes, implementing the Project work Lecture, sample applications in class 9 Information Integration Reading the lecture notes, implementing the Project work Lecture, sample applications in class 10 Opinion Mining and Sentimental Analysis Reading the lecture notes, implementing the Project work Lecture, sample applications in class 11 Web Usage Mining Reading the lecture notes, implementing the Project work Lecture, sample applications in class 12 Recent topics in Web mining and student presentations. Reading the lecture notes, preparing the Project report Student oral presentations and discussion sessions. 13 Recent topics in Web mining and student presentations. Reading the lecture notes, preparing the Project report Student oral presentations and discussion sessions. 14 Applications of Web Mining and Project presentations. Reading the lecture notes, preparing the Project presentation Student oral presentations and discussion sessions. 15 Applications of Web Mining and Project presentations. Reading the lecture notes, preparing the Project presentation Student oral presentations and discussion sessions. 16/17 Final Exam Reading the lecture notes In class written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  B. Liu, "Web Data Mining: Exploring Hyperlinks, Contents and Usage Data", 622 Pages, Second Edition, July 2011, Springer. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 0 0     Homeworks/Projects/Others 3 100 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Reaches wide and deep knowledge through scientific research in the field of computer engineering, evaluates, implements, and comments. 5 2 Describes and uses information hidden in limited or missing data in the field of computer engineering by using scientific methods and integrates it with information from various disciplines. 5 3 Follows new and emerging applications of computer engineering profession, if necessary, examines and learns them 5 4 Develops methods and applies innovative approaches in order to formulate and solve problems in computer engineering. 5 5 Proposes new and/or original ideas and methods in the field of computer engineering in developing innovative solutions for designing systems, components or processes. 5 6 Designs and implements analytical modeling and experimental research and solves the complex situations encountered in this process in the field of Computer Engineering 5 7 works in multi disciplinary teams and takes a leading role and responsibility. 4 8 Learns at least one foreign language at the European Language Portfolio B2 level to communicate orally and written 5 9 Presents his/her research findings systematically and clearly in oral and written forms in national and international meetings. 4 10 Describes social and environmental implications of engineering practice. 4 11 Considers social, scientific and ethical values in collection, interpretation and announcement of data. 5 12 Acquires a comprehensive knowledge about methods and tools of computer engineering and their limitations. 5 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 Assesment Related Works     Homeworks, Projects, Others 3 16 48     Mid-term Exams (Written, Oral, etc.) 0 0 0     Final Exam 1 20 20 Total Workload: 152 Total Workload / 25 (h): 6.08 ECTS Credit: 6