ML Lab Class 1

Lab class organization

  • Exercises and announcements on the course page
  • One exercise sheet every two weeks (first one today!)
  • To be submitted by email the day before the next exercise
  • Submission date is on the exercise sheet
  • Additional lab class dates in between exercises as needed

Lab Assignments

  • You can work in groups of up to three people
  • There will be math problems, knowledge questions and programming assignments
  • Submissions are not graded, but you are strongly encouraged to do them!

Programming Assignments

  • Working with (small) real-world datasets
  • Implementing fundamental machine learning algorithms
  • You can use any language
  • But in lab class we will use Python 3

Python

Why?

  • Easy to learn, easy to read
  • Fast to write
  • Practical for real-world ML code
  • Popular with ML practitioners

Online Tutorials:

Lab Class: Solution Submissions

Submission instructions, also found on the course page:

  • You can work in groups of up to three people
  • Solutions by email to michael.voelske@uni-weimar.de
  • Solutions without code: attach one PDF file
  • Solutions with code: attach one ZIP file, with one PDF file inside (plus the code)
  • Pay attention to use the correct file name:

lastname1-matNr1-...-ml-labX.(zip|pdf)

Example:

  • Grace Hopper (Matriculation number 12345) and Rajeev Motwani (Mat. no. 12346) work together on exercise 1
  • Their solution contains code as well as text answers

  • They send me an email with the attachment:

    hopper-12345-motwani-12346-ml-lab1.zip

  • Their zip file contains:
hopper-12345-motwani-12346-ml-lab1.zip/
├── code/
│   ├── example_algorithm.py
│   └── hello_world.py
└── hopper-12345-motwani-12346-ml-lab1.pdf

Summary

  • One regular lab class every two weeks (Thursday)
  • Exercises are due the day before (Wednesday)
  • We discuss solutions in lab class
  • Extra appointments in between (I will tell you the week before)
  • Next week: Tutorial on programming assignments
  • Now: The first exercise sheet

Python Basics: nbviewer.ipython.org/github/yoavram/CS1001.py/blob/master/recitation1.ipynb

(there are other lectures in the series: github.com/yoavram/CS1001.py/blob/master/README.md)

Homework for Next week

  • Install Python
  • Do exercise 6a
  • Bring a laptop if you want to follow along in class