applied machine learning |
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למידת מכונה יישומית |
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]
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]
Administrative Details
13:25 ‒ 13:45 break
13:45 ‒ 14:50 class
14:50 ‒ 15:10 break
15:10 ‒ 16:00 class
- 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):
13:25 ‒ 13:45 break
13:45 ‒ 14:50 class
14:50 ‒ 15:10 break
15:10 ‒ 16:00 class