Syllabus | Instructor

• Course taught in Fall'23.
• Every Tuesday and Thursday: 8:00 AM - 9:15 AM EST
• In-person classes recorded.

• Teaching Assistant, Bharadwaj Sharma Kasturi's Office Hours: Tues 9:30 AM-10:30 AM and Thurs 9:30 AM-10:30 AM. For any questions, contact via Email: bk5953(AT)g(DOT)rit(DOT)edu. Or Slack. Join via Zoom: https://rit.zoom.us/j/94233732942 or meet in person at the WAL-3470 Group Study Room.
• Nidhi's Office Hours: Right after class on Tuesday/Thursday until 9:45 am (Best Time). OR by appointment. Email: nidhi(DOT)rastogi(AT)rit(DOT)edu. Or Slack.

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 2023.

At the end of this course, students should be able to accomplish the following learning objectives:

1. Learn, analyze, and evaluate data mining and machine learning techniques by attending classroom-style lectures and working on practical assignments.
2. 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. A quick and dirty intro to Github can be found here.. These resources should help you get up to speed with what is needed for this course.

The course uses GitHub for assignment submission and Slack for discussions and questions. I will post slides, assignments, and any recorded videos here.

Textbook:

• [AG]"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition" by Aurélien Géron (2019), Link to e-Book and Github Repo. A more recent one, but does not match the book edition is here.
• [GBC]"Deep Learning," by Ian Goodfellow, Yoshua Bengio, and Aaron Courville Link to e-book
• [CB]"Pattern Recognition and Machine Learning," by Christopher M. Bishop, Link to e-Book
• [FE] "Feature Engineering for Machine Learning - Principles and Techniques for Data Scientists" Link to e-book

There is no need to buy the book, and 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. Grades will follow the RIT Grading Scale:

Late work policy:

• 0-24 hours late submission: 50% of the assigned score
• 24-48 hours late submission: 25% of the assigned score
• more than 48 hours: 0 for the submission.
• 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.

Resolving course-related issues: The students should post them on the Slack channel and not be shy about it. If you send me or the TA a Qs privately, we will respond only on the channel.

Academic Integrity Do not copy from one another or from any AI chatbot (ChatGPT, Bard, etc.) for homework or project submissions. 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. AI Chatbot policy is TBD.

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 scratch. I have tried my best to credit the source, but if you see that missing anywhere, please contact me via RIT email or on slack.