## Daftar Isi




- Bagian I Pengetahuan Dasar
- 1 Pengenalan
   - 1.1 Kecerdasan Buatan
   - 1.2 Intelligent Agent
   - 1.3 Konsep Belajar
   - 1.4 Statistical Learning Theory
   - 1.5 Training, Validation, Testing Set
   - 1.6 Supervised Learning
   - 1.7 Regresi
   - 1.8 Semi- _supervised_ Learning
   - 1.9 Un _supervised_ Learning
   - 1.10 Proses Belajar
   - 1.11 Tips
   - 1.12 Contoh Aplikasi
   - Soal Latihan
- 2 Fondasi Matematis
   - 2.1 Probabilitas
   - 2.2 Probability Density Function
   - 2.3 Expectation dan Variance
   - 2.4 Bayesian Probability
   - 2.5 Gaussian Distribution
   - 2.6 Apakah Karakteristik Sampel Mencerminkan Populasi?
   - 2.7 Teori Keputusan
   - 2.8 Hypothesis Testing
   - 2.9 Teori Informasi
   - 2.10 Matriks
   - 2.11 Bacaan Lanjutan
   - Soal Latihan
- 3 Data Analytics
   - 3.1 Pengenalan Data Analytics
   - 3.2 Nilai Atribut dan Transformasi
   - 3.3 Ruang Konsep
   - 3.4 Linear Separability DAFTAR ISI XI
   - 3.5 Seleksi Fitur
   - 3.6 Classification, Association, Clustering
   - 3.7 Mengukur Kinerja
   - 3.8 Evaluasi Model
   - 3.9 Kategori Jenis Algoritma
   - 3.10 Tahapan Analisis
   - Soal Latihan
- Bagian II Algoritma Pembelajaran Mesin
- 4 Algoritma Dasar
   - 4.1 Naive Bayes
   - 4.2 K- _mean_ s
   - 4.3 K-nearest-neighbor
   - Soal Latihan
- 5 Model Linear
   - 5.1 Curve Fitting dan Error Function
   - 5.2 Binary Classification
   - 5.3 Log-linear Binary Classification
   - 5.4 Multi- _class_ Classification
   - 5.5 Multi-label Classification
   - 5.6 Pembelajaran sebagai Permasalahan Optimisasi
   - 5.7 Batasan Model Linear
   - 5.8 Overfitting dan Underfitting
   - 5.9 Regularization
   - 5.10 Transformasi Data
   - 5.11 Bacaan Lanjutan
   - Soal Latihan
- 6 Pohon Keputusan
   - 6.1 Inductive Learning
   - 6.2 ID3
   - 6.3 Isu pada ID3
   - 6.4 Pembagian Ruang Konsep
   - Soal Latihan
- 7 Support Vector Classifier
   - 7.1 Maximal Margin Classifier
   - 7.2 Support Vector Classifier
   - 7.3 Support Vector Machine
   - 7.4 Klasifikasi lebih dari dua kelas
   - 7.5 Tips
   - Soal Latihan XII DAFTAR ISI
- 8 Hidden Markov Model
   - 8.1 Probabilistic Reasoning
   - 8.2 Generative Model
   - 8.3 Part-of-speech Tagging
   - 8.4 Hidden Markov Model Tagger
   - 8.5 Algoritma Viterbi
   - 8.6 Proses Training Hidden Markov Model
   - Soal Latihan
- 9 Seleksi Fitur dan Metode Evaluasi
   - 9.1 Feature Engineering
   - 9.2 High Dimensional Data
   - 9.3 Feature Selection
   - 9.4 Evaluasi Kinerja Model
   - 9.5 Cross Validation
   - 9.6 Replicability, Overclaiming dan Domain Dependence
   - Soal Latihan
- 10 Clustering
   - 10.1 K- _mean_ s, Pemilihan Centroid, Kemiripan Data
   - 10.2 Hierarchical Clustering
   - 10.3 Evaluasi
   - Soal Latihan
- Bagian III Artificial Neural Network
- 11 Feed _forward_ Neural Network
   - 11.1 Definisi Artificial Neural Network
   - 11.2 Single Perceptron
   - 11.3 Permasalahan XOR
   - 11.4 Multi _layer_ Perceptron
   - 11.5 Interpretability
   - 11.6 Binary Classification
   - 11.7 Multi- _class_ Classification
   - 11.8 Multi-label Classification
   - 11.9 Deep Neural Network
   - 11.10 Tips
   - 11.11 Regularization and Dropout
   - 11.12 Vanishing and Exploding Gradients
   - 11.13 Rangkuman
   - Soal Latihan
- 12 Auto _encoder_
   - 12.1 Representation Learning DAFTAR ISI XIII
   - 12.2 Singular Value Decomposition
   - 12.3 Ide Dasar Auto _encoder_
   - 12.4 Resisting Perturbation
   - 12.5 Representing Context: Word Embedding
   - 12.6 Tips
   - Soal Latihan
- 13 Arsitektur Neural Network
   - 13.1 Convolutional Neural Network
   - 13.2 Recurrent Neural Network
   - 13.3 Part-of-speech Tagging Revisited
   - 13.4 Sequence to Sequence
   - 13.5 Arsitektur Lainnya
   - 13.6 Architecture Ablation
   - 13.7 Transfer Learning
   - 13.8 Multi- _task_ Learning
   - Soal Latihan
- Bagian IV Aplikasi dan Topik Tambahan
- 14 Penerapan Pembelajaran Mesin
   - 14.1 Sistem Rekomendasi
   - 14.2 Peringkasan Dokumen
   - 14.3 Konklusi
   - 14.4 Saran Buku Lanjutan
   - Soal Latihan
- Referensi



