Structure of the lectures

flowchart TD
subgraph one[ ]
 A(linear models)--> A1(Linear regression)
 A --> A2(Polynomial Regression)
 A --> A3(Logistic Regression)
 A --> A4(Perceptron)
end
style one fill:#82c4c3,stroke-width:0px;
classDef boxes stroke-width:0px;
class A,A1,A2,A3,A4 boxes;

flowchart TD
subgraph three[ ]
 C(A Probabilistic View on Machine Learning)--> C1(Review Probability)
 C-->C2(Likelihood)
 C-->C3(Kullback Leibler Divergence)
 C-->C4(Linear Regression) 
end
style three fill:#f6d887,stroke-width:0px;
classDef boxes stroke-width:0px;
class C,C1,C2,C3,C4 boxes;

flowchart TD
subgraph two[ ]
 B(Neural Networks)--> B1(Perceptron)
 B-->B2(Deep Neural Networks)
 B-->B3(Automatic Differentiation)
end
style two fill:#82c4c3,stroke-width:0px;
classDef boxes stroke-width:0px;
class B,B1,B2,B3,B4 boxes;