Technology that automatically analyzes text has made amazing strides, and lets us do things like automatically translate from Chinese to English, summarize what people on Twitter think about some current political topic, or find clues on who the author is of some classic piece of literature. Machine learning plays a central role in this technology: software that can learn from experience. This course provides an overview of basic statistical methods for machine learning, with an emphasis on applications that have to do with text. This is a very hands-on course in which we are going to be using the Python programming language.
We will start with foundations including basic probability/statistics and python programming. The bulk of the course focuses on machine learning methods and applying them to analyze data, much of which textual. The later portion of the course will shift to surveying several tasks in natural language processing and to class projects, which will be a major component of the course. These projects will allow you to pursue your own interests (and conduct new research in so doing!).
Topics of this course include:
This course carries the Quantitative Reasoning flag. Quantitative Reasoning courses are designed to equip you with skills that are necessary for understanding the types of quantitative arguments you will regularly encounter in your adult and professional life. You should therefore expect a substantial portion of your grade to come from your use of quantitative skills to analyze real-world problems.
This course also carries the Independent Inquiry flag. Independent Inquiry courses are designed to engage you in the process of inquiry over the course of a semester, providing you with the opportunity for independent investigation of a question, problem, or project related to your major. You should therefore expect a substantial portion of your grade to come from the independent investigation and presentation of your own work.
Attendance is not required, and it is not used as part of determining the grade.
Extensions will be considered on a case-by-case basis, but in most cases they will not be granted. If an extension has not been agreed on beforehand, then for assignments, by default, 5 points (out of 100) will be deducted for lateness, plus an additional 1 point for every 24-hour period beyond 2 that the assignment is late. The maximum extension penalty is 40 points if handed in before the last day of class. Resubmissions of assignments are allowed; extension penalty applies for post-deadline resubmissions.
Note that there are always some points to be had, even if you turn in your assignment late. So if you would like to know if you should still turn in the assignment even though it is late, the answer is always yes.
You are encouraged to discuss assignments with classmates. But all coding/written work must be your own. Students caught cheating will automatically fail the course. If in doubt, ask the instructor.
The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. Please contact the Division of Diversity and Community Engagement, Services for Students with Disabilities, 512-471-6259.
A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.
Under Senate Bill 212 (SB 212), the professor and TAs for this course are required to report for further investigation any information concerning incidents of sexual harassment, sexual assault, dating violence, and stalking committed by or against a UT student or employee. Federal law and university policy also requires reporting incidents of sex- and gender-based discrimination and sexual misconduct (collectively known as Title IX incidents). This means we cannot keep confidential information about any such incidents that you share with us. If you need to talk with someone who can maintain confidentiality, please contact University Health Services (512-471-4955 or 512-475-6877) or the UT Counseling and Mental Health Center (512-471-3515 or 512-471-2255). We strongly urge you make use of these services for any needed support and that you report any Title IX incidents to the Title IX Office.
Occupants of buildings on The University of Texas at Austin campus are required to evacuate buildings when a fire alarm is activated. Alarm activation or announcement requires exiting and assembling outside. Familiarize yourself with all exit doors of each classroom and building you may occupy. Remember that the nearest exit door may not be the one you used when entering the building. Students requiring assistance in evacuation shall inform their instructor in writing during the first week of class. In the event of an evacuation, follow the instruction of faculty or class instructors. Do not re-enter a building unless given instructions by the following: Austin Fire Department, The University of Texas at Austin Police Department, or Fire Prevention Services office. Information regarding emergency evacuation routes and emergency procedures can be found at http://www.utexas.edu/emergency.
Schedule is tentative and subject to change.
Please refer to the grading policy for a high level overview about the project and requirements.