Overview

Hi! This is the homepage of the Efficient Machine Learning class taught by the Scalable Data- and Compute-intensive Analyses lab. Most information will be shared here. However, some data, e.g., data concerning examinations, will only be shared through respective FSU channels: e-mail, Moodle or Friedolin.

The contents of this homepage and the class in general are new and some are still being worked on. Let me know if you get stuck, stumble over errors, or have any other feedback:

Format

The lectures and labs take place during the following time slots:

  • Wed, 10:15AM - 11:45AM

  • Thu, 02:15PM - 03:45PM

All meetings are face-to-face in room 3220, EAP2.

The lectures will be recorded to support your learning experience. The recordings are no substitute for attending the class in-person and there are no guarantees that this will work flawlessly. In short: Only consider the recordings to be a bonus!

The class has a Matrix room hosted on the university’s Matrix server. You may join the channel with any Matrix account, including those registered with other servers. The Matrix invite to the class’s room is shared in Moodle. The room is not encrypted, and you will only see the room’s history from the time since you joined.

Special Events

What?

Date

Comment

Christi Himmelfahrt

Thu 05/18

No class

Get Together

Tue 06/27

06:30 PM, Daheme im Garten

Assignments

Week

Due Date

Mandatory Tasks

Optional Tasks

1

04/12

1.1 - 1.2, 2.1

–

2

04/19

2.2

–

3

04/26

3.1 - 3.4

–

4

05/03

4.1 - 4.4

–

5

05/10

5.1, 5.2.1, 5.2.2

5.2.3

6

05/17

6.2 - 6.3

6.1

7

05/23

7.1

–

8

05/31

7.2 - 7.3

–

9

06/07

8.1 - 8.2,

8.3.1 - 8.3.4

8.3.5

10

06/14

9.1 - 9.2

9.3

11

06/21

10.1 - 10.2

–

12

06/28

11.1 - 11.2

–

13

07/05

12.1

12.2

14

–

–

–

Submissions

We use Moodle for all submissions. You have two options:

  1. Upload a single tar.xz file to Moodle which contains your submission.

  2. Submit a link to a public git repository containing your submission. Use the text form in Moodle for the link. Note: You must submit a link every week to notify us of the submission, even if it stays unchanged.

Literature

The majority of the class provides an in-depth look behind the scenes of modern machine learning. This means, that we’ll discuss certain parts of the machine learning toolchain very extensively while neglecting others completely. More comprehensive overviews on deep learning are, e.g., available at the following locations:

Efficient Machine Learning Community

For those interested, some events and activities: