/ Recent content in Home on Class Homepage Hugo -- gohugo.io en-us /docs/grading/categories/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/grading/categories/ Categories # Assignments (80%) # We have 5 big assignments in total (subject to change). Visit this course’s Canvas and Gradescope site for the assignment documents. See the course schedule for deliverable due dates. [10%] HW1: Week 1 to Week 2 topics [15%] HW2: Week 3 to Week 4 topics [20%] HW3: Week 5 to Week 6 topics [20%] HW4: Week 7 to Week 11 topics [15%] HW5: Week 12 to Week 13 topics We do not release solutions for homework. /docs/resources/collection/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/resources/collection/ Resource Collection # Recommended Reading # All content and course materials can be accessed online. There is no textbook for this course. All Georgia Tech students have FREE access to https://www.oreilly.com, where you can find a huge number of highly rated and classic books (e.g., the “animal” books) from O’Reilly and Pearson covering a wide variety of computer science topics, including some of those listed below. Just log in with your official GT email address, e. /docs/course-info/course-overview/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/course-info/course-overview/ Course Goals # Analyze NLP techniques and apply them to text data. The course is divided into three main categories: s Preprocessing: Demonstrate how to clean and integrate text data Processing: Apply NLP algorithms on your pre-processed data to perform different tasks Post-processing: Evaluate your developed NLP models. Solve problems with real datasets Apply practical know-how (useful for jobs, research) through significant hands-on programming assignments Course Pre- and/or Co-Requisites # Review our “warnings” before taking this course. /docs/guidelines/general/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/guidelines/general/ General # Attendance # This is a fully online course. Login on a regular basis to complete your work, so that you do not have to spend a lot of time reviewing and refreshing yourself regarding the content. Class Deliverables # All class deliverables will be handled via Gradescope except quizzes which will be on Canvas. The time span offered to complete the course objectives is plentiful and deadlines will not be extended under any circumstances. /docs/course-info/instructors-and-tas/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/course-info/instructors-and-tas/ Instructors and TAs # Instructor # Max Mahdi Roozbahani mahdir@gatech.edu https://mahdi-roozbahani.github.io/ Wafa Louhichi wlouhichi3@gatech.edu https://www.linkedin.com/in/wafa-louhichi/ Nimisha Roy nroy9@gatech.edu https://nimisharoy9.wixsite.com/myportfolio Head TAs # Rusty Utomo rutomo6@gatech.edu TAs # Rohit Das rohdas@gatech.edu Neill Killgore ckillgore3@gatech.edu Emre Duman eduman8@gatech.edu Matthew Peng hpeng73@gatech.edu Karishma Thakrar kthakrar3@gatech.edu /docs/guidelines/office-hours/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/guidelines/office-hours/ Office Hours # Overview # TAs plan to hold office hours starting week 2, except on Georgia Tech holidays (e.g., thanksgiving, MLK day, spring break). Each office hour session will be run by at least one TA, and is 1 hour long. See GT’s academic calendar for the full list of holidays https://registrar.gatech.edu/calendar. We will spread the office hours across weekdays, and across time of the day. We will announce the office hour times. /docs/course-info/course-schedule/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/course-info/course-schedule/ Schedule # Important All deadline and due dates in this course will be at AoE time zone. For all dates used in this course, their times are 23:59 Anywhere on Earth (11:59 pm AoE). For example, a due date of “January 8” is the same as “January 8, 23:59pm AoE”. Convert the times to your local times using a Time Zone Converter. Scroll horizontally to see the full schedule table on mobile devices Week Dates Topics Homework Quizzes Readings 1 1/12 - 1/16 Course introduction Text data preprocessing: Normalization, lemmatization, stemming, stop words removal… Text Representations: One hot encoding BoW (frequency counting) TF-IDF HW1 out 1/16 Quiz 0 | Knowledge-based topics | out 1/12 - due 1/16 Chapter 1 Introduction to Natural Language Processing by Jacob Eisenstein Chapter 2. /docs/guidelines/diversity-and-inclusion/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/guidelines/diversity-and-inclusion/ Diversity & Inclusion # Just as machine learning algorithms cannot accomplish complex tasks if trained on datasets of limited variability, our course cannot be successful without appreciating the diversity of our students. In this class we aim to create an environment where all voices are valued, respecting the diversity of gender, sexuality, age, socioeconomic status, ability, ethnicity, race, and culture. We always welcome suggestions that can help us achieve this goal.