Instructor | Susan (Xueqing) Liu (xueqing.liu AT stevens DOT edu) |
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Susan's Office Hour |
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This course is a graduate-level course on fundamental techniques in information retrieval and text mining. By taking this course, students learn how to crawl, clean, process, mine, and infer knowledge from a massive amount of text data; how to build a search engine from scratch, including indexing, building retrieval models, and evaluating the performance of a search engine; they will also learn important machine learning and deep learning techniques for text data, including topic model, LSTM and BERT; finally, they will learn state-of-the-art research topics in text mining and information retrieval, and get research experience in these topics by working on the final project.
Learning Goals: Upon successful completion of this course, students should be able to:
Recommended reading:
Homework | 40% |
Midterm | 30% |
Project | 30% |
Late Policy: submit within 24 hours of deadline - 90%, within 48 hours - 70%, over 48 hours - 0 point, 0 if code not compile
Academic Integrity: Students must follow the instructions from the (Stevens Honor system). This course will have a zero-tolerance policy regarding plagiarism. You should complete all the assignments and quizzes on your own. You can help your classmates with questions such as how to use the programming language, what the library classes or methods do, what the errors mean, and how to interpret the assignment instructions. You are encouraged to come to both the instructor and the CAs' office hours regarding any questions you have, or email your questions to both the instructors or the CAs. However, you may not give or receive help from others (except the CA) with the actual implementation or answers for any of the assignments or tests. Do not show or share your code with others, and do not view or copy source code from others. For the same reason, you are not allowed to copy and paste a code snippet you found online in the assignments. All electronic work submitted for this course will be archived and subjected to automatic plagiarism detection. Whenever in doubt, please seek clarifications from the instructor. Students who violate the academic intergrity principle of Stevens will be immediately reported to the department and the school (which could leave a permanent mark on the transcript).
If you need special accommodations because of a disability, please contact the instructor through emails.