Content
A technical analytics course that will teach supervised and unsupervised learning strategies, including regression, classification, generalized linear models, regularization, dimension reduction methods, trees, and clustering. Advanced analytical methods are shown in practice: e.g., neural networks and Gaussian processes.
Grading details
Rubric
- 35% Homework
- 20% Exam #1, roughly half-way through
- 20% Exam #2, roughly 3/4 through
- 25% Take Home Final Exam/Project (TBD), due during finals week
Grading will nominally follow the typical breakdown on a total percentage scale, e.g., [93-100 A), [90-93 A-), [87-90 B+), [83-87 B), etc. All grades in Canvas will follow this scheme. However the instructor reserves the right to apply a final curve in the students’ favor.
Exams
- There will be two written exams and a take home final project, TBD. All in-class exams will be closed book and closed notes (with the exception of a cheat sheet). The take home final will be open book/notes, however students may not collaborate with anyone else. What is turned in must be entirely their own work.
- During exams, students should be considerate of their classmates by arriving on time. If a student arrives after at least one student has finished the exam and left the room, he/she will NOT be allowed to take the exam and will receive a grade of zero. Cell phones should be turned off before entering the exam room.
- Make-up exams will be offered for students who have well-documented emergencies approved by the instructor or reported in advance.
Homework
- Homework will be assigned and due on a regular basis. Students are welcome to collaborate with one another, but are required to submit their own work as well as be able to reproduce it.
- All work must be shown and software must be used, when appropriate, with attached software output.
- All homework grades will be kept which means NO homework grade will be dropped.
- All homework must be submitted on-time on Canvas.