Homework will be posted on Canvas. Each homework will consist of two parts: (1) a set of conceptual questions, and (2) programming. For the programming part, we will provide a code template (and sometimes partial code as well) in a Jupyter notebook. You will have two weeks to complete each homework assignment.

Getting help: You can visit the instructor and the TAs during office hours as well as post questions on Piazza to get help on the assignments. Regarding help from fellow students, see the note on collaboration below.

Collaboration: All assignments are to be written individually, except the Case Study (which can be done in groups of up to 3 members). Collaboration and study groups are allowed and encouraged. However, each student should submit their own write-up. Please see the collaboration policy for details.

Submitting: We ask that you submit two files per homework: (1) a pdf file with your answers to the conceptual questions, and (2) the Jupyter notebook we provide as a template with all your code that you filled in. Both files (.pdf and .ipynb) are to be uploaded electronically only on Gradescope (no hard copy print outs).

Homework assignments are due at the beginning of the class on the day it is due. You can upload your files multiple times, but note that we will use the latest upload date as the submission date, which may factor into your slip days accordingly. Please see the late submission policy for details.

There will be a midterm exam (in class) and a final exam (to be scheduled by the University).

Note: For the midterm, you are allowed to bring with you 2 A4-size sheets, containing your own notes (hand-written or typed). You can use both sides of each sheet. For the final, you are allowed to bring up to 5 A4-size sheets (double sided), again containing your own notes. Use of any computers or other electronic devices during the exams is not allowed. The tentative dates are posted above, the finalized dates will be announced during the semester.

Starting the second half of the course (after Spring break), we will provide you with information describing a large dataset and a list of potential questions to address based on this dataset. We will also release the dataset after Spring break. You will be given the second half of the semester to complete your analysis and modeling on the data. Particularly, you will be expected to carefully choose to apply the techniques and tools you have learned throughout the course to address the problems of interest using machine learning.

The Case Study will consist of 3 Phases. In Phase I, you will think about the data and the problem at hand and brainstorm. In Phase II, you will do hands-on data cleaning, preparation and exploratory analysis and data understanding. In Final Phase III, you will build predictive models using various machine learning tools you have learned throughout the course.

Evaluation: We will assess your case study outcomes in terms of your analytical approach to the problems, and not only based on the quality of your results. That is, the emphasis will be on evaluating how methodical you were in your analysis in terms of the tools you chose to apply, in the way you draw conclusions from your own results, and the sequence of steps you took based on your analyses and intermediate results. We will also assess if you used the best practices in building your solutions, including proper model selection, model comparisons to appropriate baselines, choice of evaluation metrics, and so on.

Teams: The Case Study can be done in groups of up to 3 students. We recommend forming groups of 3. but groups of 2 students should also be fine. We do not recommend single-member teams given the amount of workload. You can use Piazza for communication toward finding team members. Submitting: You are asked to submit a single Jupyter notebook, composed of all your code and results, along with a pdf file with answers to specific questions. All submissions will be made on Canvas.