QTM 3601-01 - DEEP LEARNING IN BUS

Academic Period:

Fall 2025

Section:

Title:

DEEP LEARNING IN BUSINESS

Meeting Patterns:

Tue/Thu | 11:30 - 13:00

Locations:

Gerber Hall 103 Can be several values or empty

Start Date:

Tuesday, August 26, 2025 Date format can be changed

End Date:

Friday, December 12, 2025 Date format can be changed

Instructor Name:

Vicky Zhu

Instructor Email:

vzhu@babson.edu

Academic Unit:

MAST - Mathematics, Analytics, Science and Technology

Academic Level:

Undergraduate

Maximum Credits:

4

Delivery Mode:

In-Person

Allowed Grading Bases:

Graded

Section Status:

Waitlist

Enrollment Count:

30

Section Capacity:

30

Description:

QTM3601 Deep Learning in Business 4 Advanced Liberal Arts Credits This course is dedicated to learning a type of artificial intelligence through building neural network models that mimic the human brain to solve complex business problems, which involves a variety of data types like text, image, sequential, etc. The course will build on analytical concepts learned from the AQM2000 (Predictive Business Analytics) course and introduce other unsupervised and self-supervised machine learning concepts in types of neural networks, natural language processes, and reinforcement learning. Each concept contains topics like model building and parameter tuning through optimization, regularization, etc. These advanced topics will be discussed in the context of practical real-world applications such as prediction, classification, image recognition, text analysis, gaming, etc. The implementation of the introduced topics will be carried out in Python programming language. Prerequisites: AQM 2000

HTML Description:

QTM3601 Deep Learning in Business

4 Advanced Liberal Arts Credits

This course is dedicated to learning a type of artificial intelligence through building neural network models that mimic the human brain to solve complex business problems, which involves a variety of data types like text, image, sequential, etc. The course will build on analytical concepts learned from the AQM2000 (Predictive Business Analytics) course and introduce other unsupervised and self-supervised machine learning concepts in types of neural networks, natural language processes, and reinforcement learning. Each concept contains topics like model building and parameter tuning through optimization, regularization, etc. These advanced topics will be discussed in the context of practical real-world applications such as prediction, classification, image recognition, text analysis, gaming, etc. The implementation of the introduced topics will be carried out in Python programming language.

Prerequisites: AQM 2000

Format:

In-Person Can be several values or empty

Session:

Full Session Can be several values or empty

Elective:

Advanced Elective (UGrad) Can be several values or empty

Program:

Advanced Liberal Arts (UGrad) Can be several values or empty