Machine Learning (SS 2017)

Inhalt

Lecturer

Organization

News

Lectures (tentative schedule)

DateTopicSlidesLiterature
20.04.2017 Chapter 1: Introduction
  • What is Machine Learning?
  • Structured vs. unstructured data
  • Nominal, ordinal, and numerical features
  • Supervised vs. unsupervised learning
Chapters 1 in [1,2,4]
27.04.2017 Chapter 2: Regression
  • Ordinary Least Squares
  • Gradient Descent
Chapter 10 in [3]; Chapter 8 in [4]
04.05.2017
  • Multiple Linear Regression
  • Handling Non-Numerical Features
  • Polynomial Regression
  • Evaluation Fundamentals
  • Regularization
Chapters 6 & 10 in [3]; Chapter 8 in [4]
11.05.2017 Chapter 3: Classification
  • Logistic Regression
  • Evaluating Classifiers
Chapter 5 & 7 in [4]; Chapters 3 & 6 in [3]
18.05.2017
  • k-Nearest-Neighbors
  • Naïve Bayes
Chapter 2 & 4 in [4]; Chapter 3 in [3]
25.05.2017 Ascension Day
01.06.2017
  • Decision Trees
  • Ensemble Learning
Chapter 3 & 7 in [4]; Chapter 3 & 7 in [3]
08.06.2017 Chapter 4: Clustering
  • k-Means
  • Evaluating Clusterings
  • Hierarchical Clustering
Chapter 10 in [4]; Chapter 11 in [3]; Chapter 13 & 14 in [2]
15.06.2017 Corpus Christi
22.06.2017
  • Density-based Clustering
Chapter 11 in [3]; Chapter 15 in [2]
29.06.2017 Chapter 5: Next Steps
  • Resources to learn more
Q & A

Exercises (tentative schedule)

DateDescriptionDownload
27.04.2017 Crash Course in Python
11.05.2017 Exercise 1
18.05.2017 Programming Assignment 1  
01.06.2017 Exercise 2  
08.06.2017 Programming Assignment 2  
22.06.2017 Exercise 3  
29.06.2017 Programming Assignment 3  

Exam

Resources

Literature