- Course taught in Fall'21.
- Every Tuesday and Thursday: 8:00 AM - 9:15 AM EST
- Teaching Assistant, Rigved Rakshit's Office Hours: Tuesday, Thursday: 10:00 AM - 11:00 AM EST, on Zoom
- Nidhi's Office Hours: Tuesday: 9:15 AM - 10:15 AM EST, Rm 1573 or Zoom
This is a foundation course in data science, which emphasizes both concepts and techniques. The course provides an overview of data analysis tasks and the associated challenges, including data preprocessing, model building, validation, and evaluation. Major families of data analysis techniques covered include classification, clustering, association analysis, anomaly detection, and statistical testing. The course consists of a series of programming assignments that will involve implementing specific techniques on practical datasets from diverse application domains, reinforcing the concepts and techniques covered in lectures. The best way to learn an algorithm is to implement and apply it yourself. You will experience that in this course. This course is taught at Rochester Institute of Technology in Fall 2021.
At the end of this course, students should be able to accomplish the following learning objectives:
- Learn, analyze, and evaluate data mining and machine learning techniques by attending classroom-style lectures and working on practical assignments.
- Develop critical, entry-level skill sets required to solve real-world problems that utilize machine learning.
Knowledge of Python and Github is required. An excellent primer for python can be found here. And a quick and dirty intro to Github can be found here.. I have not used it, but this comes highly recommended - [python programming] (https://developers.google.com/edu/python/Together, they should help you get started.)
| Activity | Expected Time(hrs/wk) |
|---|---|
| Lectures | 2.5 hours |
| Assignments (first 9 weeks) | 1-5 |
| Project (rest of the weeks) | 1-5 |
The course uses GitHub for assignment submission, discussions, and questions. I will post slides assignments and any recorded videos here.
Textbook:
- "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition" by Aurélien Géron (2019), Link to e-Book
- "An Introduction to Statistical Learning with Applications in R" by: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (e-book made available by the authors)
- "Pattern Recognition and Machine Learning", by Christopher M. Bishop
There is no need to buy the book, they're for reference only. The lecture slides, links should cover all the material you need to do well in the class. I am open to feedback if you need more detailed content or a different course presentation format. Grading: Evaluation will be based on the following distribution:
| Activity | % Distribution |
|---|---|
| Assignments | 60% |
| Final project report and presentation | 35% |
| Class Participation | 5% |
Late work policy:
- 0 Credit for the Assignment or Project if submitted after deadline.
Exceptions: Under extraordinary circumstances involving self, a friend, or a family member. Highly creative excuses will be given a few minutes of attention and nothing further.
Teamwork: The instructor will form small study groups after the 1st class; the group distribution will be random. Group members can discuss the problem but not their approaches or solutions. Any unresolved question should b posted as an issue on Github.
Academic Integrity Please do not copy from one another; you're doing yourself a great deal of disservice and losing precious time to build a skillset. Refer to this link to learn about RIT's policy for Academic Integrity.
Accommodations for students with disabilities: Please discuss a requirement for any accommodation due to a disability early in the semester. You will require an accommodations letter from the Disability Resources office before you reach out to me. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, we encourage you to contact them at [email protected].
Self-care is Important: We want you to succeed in this course, but most importantly, enjoy the process of exploration and learning. Self-care through exercise, going out for walks and getting the sun while you can, enjoying nature, regularly checking in with family and friends about how you feel, stepping away from technology every once in a while and maintaining a healthy diet- all are part of keeping a balanced mental and physical health. RIT provides several helpful resources, do not hesitate to ask for help from us. Some of the links are given below:
- Counseling and Psychological Services (CaPS)
- Call 585-475-2261 for urgent matters or email [email protected] for non-urgent cases.
Disclaimer Not all material has been created from scrath. I have tried my best to credit the source, but if you see that missing anywhere, please contact me via RIT email.