Date | Topic | Slides | Literature |
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 |
|
 |
Chapter 11 in [3]; Chapter 15 in [2] |
29.06.2017 |
Chapter 5: Next Steps
Q & A
|
 |
|