Course in Foundations of Machine Learning

• General Information
• Lecturenotes
• Lab

General Information

Organization

• The lecture will take place in attendance. It will also be recorded and uploaded afterwards.
• In addition to the lecture, you will be provided with both theoretical and practical exercises. We will not collect or grade these exercises, but provide you with solutions and helpful tutorial videos (released at the same time). Nevertheless, you will still need to solve the exercises on your own/with a partner. You are responsible for your own learning success.
• We expect you to have studied the lecture, solved the exercises and reviewed the solutions before the corresponding lab session takes place. The lab sessions will be done in a flipped classroom format. This only works if you are well prepared. Please bring a laptop, tablet or smartphone to participate. The lab sessions will not be streamed or recorded.
  The dates of your group's lab sessions can be found in the following table:

  Topic	Group A	Group B	Group C	Group D
  1	25.10.2023	18.10.2023	24.10.2023	17.10.2023
  2	08.11.2023	01.11.2023	14.11.2023	07.11.2023
  3	29.11.2023	15.11.2023	28.11.2023	21.11.2023
  4	13.12.2023	06.12.2023	12.12.2023	05.12.2023
  5	10.01.2024	03.01.2024	09.01.2024	19.12.2023
  6	24.01.2024	17.01.2024	23.01.2024	16.01.2024

  
  We might be forced to close single groups (A, B, C, D) during the semester due to a lack of attendance. Remaining participants will then be asked to join another group.
• Please also communicate with your fellow students to exchange about the course topics, the exercises and solution approaches.
  • Lecture website - materials and announcements will be uploaded on this website.
  • Email - important announcements will be sent out via email to students that are officially enrolled via AlmaWeb. Please notify Niklas Deckers immediately if you did not receive the welcome email at the start of the semester.
  • Discord - you are invited to engage in discussions with your fellow students. We provide a Discord server that you are invited to join. The link will be provided in the welcome email.

Exam

• There will be a take-home mock exam that we are planning to release on 23.01.2024.
• It will be discussed in the lecture slot on 30.01.2024.
• The exam will be in German, but you are also allowed to write your answers in English.
• Please remember to bring your passport/Personalausweis and your student ID.
• You are allowed to bring a handwritten sheet (DIN A4, one side only) with useful information into the exam. It must be written by hand by yourself. The sheet will be collected together with the exam at the end of the exam.
• You are also allowed to bring a non-programmable calculator.
• If needed (international students), you may bring a dictionary.

Requirements

Prerequisites for this course are the basic modules about algorithms and data structures, theoretical computer science and mathematics. We will use Python as a programming language.
It might be helpful to refresh the following topics as their use will be required in the course. This list might be incomplete.

• Mathematical set notation
• Boolean formulas
• Matrix multiplication and transposition, matrix-vector multiplication
• Partial derivatives, rules of derivation, Taylor formula
• Straight line equation: Formulating the equation, drawing and reading a plot; quadratic functions
• Laws of exponents, laws of logarithms
• Probabilities: Product rule, conditional probabilities, independence, total probability, Bayes Theorem

Lecturenotes

• Machine Learning » Introduction » Organization, Literature [video from last year]
• Machine Learning » Introduction » Learning Tasks [video from last year]
• Machine Learning » Introduction » Elements of Machine Learning [video from last year]
• Machine Learning » Introduction » Syntax & Model Overview
  
  
• Machine Learning » Machine Learning Basics » Concept Learning [video from last year]
• Machine Learning » Machine Learning Basics » From Regression to Classification [video from last year]
• Machine Learning » Machine Learning Basics » Evaluating Effectiveness [video from last year]
  
  
• Machine Learning » Linear Models » Logistic Regression [video from last year]
• Machine Learning » Linear Models » Overfitting and Regularization [video from last year]
• Machine Learning » Linear Models » Gradient Descent [video from last year]
  
  
• Machine Learning » Bayesian Learning » Probability Basics [video from last year]
• Machine Learning » Bayesian Learning » Bayes Classifier [video from last year]
• Machine Learning » Bayesian Learning » Frequentist versus Subjectivist
  
  
• Machine Learning » Decision Trees » Decision Trees Basics [video from last year]
• Machine Learning » Decision Trees » Impurity Functions [video 1 from last year] [video 2 from last year]
• Machine Learning » Decision Trees » Decision Trees Algorithms [video from last year]
• Machine Learning » Decision Trees » Decision Trees Pruning [video from last year]
  
  
• Machine Learning » Neural Networks » Perceptron Learning [video from last year]
• Machine Learning » Neural Networks » Multilayer Perceptron [video from last year]
• Machine Learning » Neural Networks » Advanced MLPs [video from last year]

Lab

The specific dates of the lab sessions can be found here: Organization.

Topic 1: Intro, Python and Maths Basics

• Material will be published on: 10.10.2023
• Relevant lectures: Until Introduction > Learning Tasks

Topic 2: Concept Learning & Evaluation

• Material will be published on: 24.10.2023
• Relevant lectures: Until Machine Learning Basics > Evaluating Effectiveness

Topic 3: Linear Models

• Material will be published on: 07.11.2023
• Relevant lectures: Until Linear Models > Overfitting & Regularization

Topic 4: Bayesian Classification

• Material will be published on: 28.11.2023
• Relevant lectures: Until Bayesian Learning > Bayes Classifier

Topic 5: Tree-Based Classification

• Material will be published on: 12.12.2023
• Relevant lectures: Until Decision Trees > Pruning

Topic 6: Neural Networks

• Material will be published on: 09.01.2024
• Relevant lectures: Until Neural Networks > Multilayer Perceptron