TECH 27 — Applied Machine Learning with Python for AI Applications
Quarter: Fall
Instructor(s): Ishaani Priyadarshini
Date(s): Sep 23—Nov 11
Class Recording Available: Yes
Class Meeting Day: Wednesdays
Grade Restriction: No letter grade
Class Meeting Time: 6:00—7:30 pm (PT)
Tuition: $590
Refund Deadline: Sep 25
Unit(s): 1
Status: Registration opens Aug 17, 8:30 am (PT)
Quarter: Fall
Day: Wednesdays
Duration: 8 weeks
Time: 6:00—7:30 pm (PT)
Date(s): Sep 23—Nov 11
Unit(s): 1
Tuition: $590
Refund Deadline: Sep 25
Instructor(s): Ishaani Priyadarshini
Grade Restriction: No letter grade
Recording Available: Yes
Status: Registration opens Aug 17, 8:30 am (PT)
Machine learning has evolved from a technical specialty into an essential decision-making tool for business leaders. This course uses Python to equip professionals with both technical skills and strategic frameworks for effective decision-making. Through hands-on exercises, you'll master essential techniques in regression, classification, and advanced algorithms in deep learning. Students will implement and test over 15 different machine learning methods, gaining practical experience through real-world case studies in finance, healthcare, ecommerce, and marketing and interactive projects selected to reflect real-world business challenges. We will explore both supervised and unsupervised learning techniques, with assignments tailored to accommodate varying experience levels. Students will complete a customizable final project that aligns with their professional goals.
ISHAANI PRIYADARSHINI
Assistant Professor, School of Electrical & Computer Engineering, Washington State
Ishaani Priyadarshini received a PhD in electrical and computer engineering from the University of Delaware, where she focused on technological singularity. She completed postdoctoral research at UC Berkeley, exploring trustworthy and fair AI systems. She has taught at UC Berkeley's School of Information and was a course facilitator for Cornell’s online certificate programs known as eCornell. She specializes in AI, data science, and cybersecurity. Textbooks for this course:
There are no required textbooks; however, some fee-based online readings may be assigned.