Time: Mon/Wed 10:15am-11:45am

Location: AGH Auditorium 106B

Instructors:
Prof. Dinesh Jayaraman

Teaching Assistants:
We have the following wonderful team of TAs this semester

• Daniel Alexander (alexdan@seas.upenn.edu)
• Jessica Liang (jeliang@seas.upenn.edu)
• Emma Shedden (eshedden@seas.upenn.edu)
• Jiayi Xin (jiayixin@seas.upenn.edu)
• Alan Zhu (alzhu@seas.upenn.edu)
• Dominic Chang (domchang@seas.upenn.edu)
• Elliu Huang (elliuh@seas.upenn.edu)
• Shivi Jain (shivij@seas.upenn.edu)
• Wendy Deng (haoyund@sas.upenn.edu)
• Tuen Yue Tsui (tytsui@seas.upenn.edu)
• Yuanming Shao (shaoym@seas.upenn.edu)

You can get to know them through this introductory slide deck.

We are currently holding the office hours listed below. Please note that the schedule is subject to change. For updated schedules please refer to the CIS 4190/5190 Fall 2025 Student Calendar.

Name	Time	Location
Prof. Dinesh Jayaraman	Monday 11:45am-12:45pm	AGH 329
Shivi Jain	Monday 4:00pm-5:00pm	Levine 501
Dominic Chang	Monday 5:15pm-6:15pm	Levine 501
Jiayi Xin	Tuesday 12:00pm-1:00pm	Levine 601
Elliu Huang	Tuesday 2:00pm-3:00pm	Levine 612
Alan Zhu	Wednesday 9:00am-10:00am	Levine 5th floor bump space
Yuanming Shao	Wednesday 1:00pm-2:00pm	Levine 501
Wendy Deng	Wednesday 3:30pm-4:30pm	Levine 601
Emma Shedden	Wednesday 5:00pm-6:00pm	Levine 5th floor bump space
Daniel Alexander	Thursday 10:00am-11:00am	Levine 3rd floor bump space
Tuen Yue Tsui	Thursday 2:30pm-3:30pm	Levine 501
Jessica Liang	Friday 11:00am-12:00pm	Levine 3rd floor bump space

Links: We will use Ed Discussion for questions and communication, and GradeScope to submit assignments. Canvas is just the “official” LMS of the university, but in practice we will not use it very much after the first week. It serves as the hub for Gradescope, Ed Forum etc. All other materials will be posted on this course website. We encourage students to use Google Colab for coding assignments.

Waitlist: (Message dated Aug 26, 2025) Everyone on the waitlist as of Aug 25, 2025 has received e-mail communication about the status.

Attendance: We expect students to attend classes regularly, and some portion of the grade will come from regular in-class quizzes.

Description: Machine learning has been essential to the success of many recent technologies, including autonomous vehicles, chatbots, robotics, search engines, genomics, automated medical diagnosis, image recognition, and social network analysis. This course will introduce the fundamental concepts and algorithms that enable computers to learn from experience, with an emphasis on their practical application. It will introduce supervised learning (linear and logistic regression, decision trees, neural networks and deep learning), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning.

CIS 4190 vs. 5190: This course has an undergraduate version (CIS 4190) and a graduate version (CIS 5190). The lectures are the same, but you may be evaluated differently on your homeworks and projects; in particular, some homeworks will have components that are mandatory for CIS 5190 but optional for CIS 4190. Importantly, since the two versions have different requirements, you cannot complete the course as CIS 4190 and petition afterwards to have it changed to CIS 5190 for graduate credit.

Prerequisites: Introductory probability and statistics, multivariable calculus, and linear algebra are required (HW 0 will test your knowledge of this material). In addition, you are expected to be able to program comfortably in some language. We will use Python throughout the course, and can help you pick it up (primer + office hours). If you are not confident of your coding skills in any language at all, the homework may be very difficult.

Textbook: There is no required textbook, but you can find useful resources here.

All policy will be documented in one unified location in the administrivia slides presented on the first day of class, which will be updated if need be. See the schedule here