Moshe Sipper
  • Home
  • Publications
  • Books
  • Research
  • Teaching
  • Blog
  • Comic Sip
  • Songs

applied machine learning

Picture

למידת מכונה יישומית
לתלמידי תואר ראשון ושני

Lecturer: Prof. Moshe Sipper
202.2.6511, ​Spring 2022 (Semester Bet)
Syllabus
This course covers the applied side of algorithmics in machine learning and deep learning, focusing on hands-on coding experience in Python. [Official syllabus page]
Lesson plan​​
Resources
Administrative Details
  • Prerequisites: Algorithms, Linear algebra, Probability
  • Good to know, though not a prerequisite: Python
  • Attendance is not mandatory: You may view classes (live) online, and peruse class recordings on Moodle
  • Credits: 4
  • ​​Grade:
    • 70%: Final exam
    • 30%: Homework (3 coding assignments)
    • You must pass both course components in order to pass the course
  • Classroom: 34/2
  • Schedule (non-standard):
               12:10 ‒​ 13:25 class
               13:25 
‒ 13:45 break
               13:45 
‒ 14:50 class
               14:50 
‒ 15:10 break
               15:10 
‒ 16:00 class 
  • Home
  • Publications
  • Books
  • Research
  • Teaching
  • Blog
  • Comic Sip
  • Songs