1Chapter 1. Machine Learning
344.5 Redundant arms and hands
21.1 Introduction
354.6 Vision-based robot navigation and positioning
31.2 Why Is Machine Learning Important?
364.7 Computational models of human walking
41.3 Supervised learning
374.8 Statistical Model
51.4 Unsupervised learning
384.10 Another Computational Model of Human
61.5 Artificial Neural Network (ANN)
39Walking
71.6 Linear Regression
404.11 Exercise
81.7 Logistic Regression
41Chapter 5. Natural Language Processing
91.8 Exercise
425.1 Introduction
10Chapter 2. Deep Learning
435.2 Neural Machine Translation
112.1 Introduction
445.3 Human-Computer Interaction
122.2 Deep Learning Revolution
455.4 Knowledge and Common Sense
132.3 Artificial Neural Network
465.5 Low resource NLP tasks
142.4 Deep Neutral Network
475.6 Multimodal Learning
152.5 Automatic speech recognition
485.10 Exercise
162.6 Image recognition
49Chapter 6. Computer Vision
172.7 Visual Art Processing
506.1 Introduction
182.8 Exercise
516.2 Image enhancement
19Chapter 3. Reinforcement Learning
526.3 Transformations
203.1 Introduction
536.4 Filtering, Fourier \wavelet transforms\
213.2 Learning by imitation
54image compression
223.3 Domain Randomization
556.5 Color vision
233.4 Lifelong learning
566.6 Feature extraction
243.5 Meta-Learning
576.7 OpenCV
253.6 Security Learning
586.8 Pose estimation
263.7 In-depth model-based methods
596.9 Pose Estimation with Deep Learning
273.8 Exercise
606.10 Registration
28Chapter 4. Robotics
616.11 Traditional Feature-based Approaches
294.1 What is robotics?
626.12 Other methods
304.2 What is a robot?
636.13 Exercise
314.3 Generation of Movement and Control of
64Glossary
32Anthropomorphic Organizations
65Index
334.4 Humanoid Robots
