Yapay Zekâ
Klasik Makine Öğrenmesi
Classical ML alanının bir parçası, seviye 2. Aşağıdaki konular boyunca yaklaşık 7 saatlik çalışma.
Neighbors And Bayes
- k-Nearest NeighborsCloseness votes: the query point takes the label of its neighborhood.1–2 sa
- Naive BayesGenerative classification under a bold assumption: features independent given the class.1–2 sa
Trees And Ensembles
- Decision TreesQuestions in sequence: splits, impurity intuition, and depth as capacity.1–2 sa
- Random ForestsMany jittery trees, one calm vote: bagging plus feature randomness tames variance.1–2 sa
- Gradient BoostingWeak learners, sequential correction: each round fits what remains wrong.1–2 sa
Margins And Kernels
- Support Vector MachinesThe widest street between classes: margins, support vectors, and the points that matter.1–2 sa
- KernelsSimilarity as features: lifting data so lines can separate what curves must.1–2 sa