| 週次 |
授課內容 |
| 第1週 |
I. Evolution of Technologies: Automation
1. Four inventions: Compass, gunpowder, paper, printing
2. Agricultural revolution
3. Steam engine (Industrial revolution)
4. Automatic control (mechanical components)
5. Electricity, electric motors, electricity supply
6. Automatic control (mechanical and electrical components)
7. Monster computers and information systems (LEO and Univac)
8. Internet 1965-1983 (Computer communication, computational resources sharing, email)
9. Personal computers and personalized systems
10. Automatic control (mechanical and electrical components equipped with programmable electronic controllers)
11. Internet and World Wide Web (Websites, application software sharing, e-commerce, cross-border information systems, Web 2.0)
12. Robots (Robotic arms, entertainment robots, humanoid robots) |
| 第2週 |
I. Evolution of Technologies: Automation
1. Four inventions: Compass, gunpowder, paper, printing
2. Agricultural revolution
3. Steam engine (Industrial revolution)
4. Automatic control (mechanical components)
5. Electricity, electric motors, electricity supply
6. Automatic control (mechanical and electrical components)
7. Monster computers and information systems (LEO and Univac)
8. Internet 1965-1983 (Computer communication, computational resources sharing, email)
9. Personal computers and personalized systems
10. Automatic control (mechanical and electrical components equipped with programmable electronic controllers)
11. Internet and World Wide Web (Websites, application software sharing, e-commerce, cross-border information systems, Web 2.0)
12. Robots (Robotic arms, entertainment robots, humanoid robots) |
| 第3週 |
I. Evolution of Technologies: Automation
1. Four inventions: Compass, gunpowder, paper, printing
2. Agricultural revolution
3. Steam engine (Industrial revolution)
4. Automatic control (mechanical components)
5. Electricity, electric motors, electricity supply
6. Automatic control (mechanical and electrical components)
7. Monster computers and information systems (LEO and Univac)
8. Internet 1965-1983 (Computer communication, computational resources sharing, email)
9. Personal computers and personalized systems
10. Automatic control (mechanical and electrical components equipped with programmable electronic controllers)
11. Internet and World Wide Web (Websites, application software sharing, e-commerce, cross-border information systems, Web 2.0)
12. Robots (Robotic arms, entertainment robots, humanoid robots) |
| 第4週 |
II. Evolution of Technologies: ICT
1. 4G LTE, 5G; WiFi, WiMax
2. Multicore CPU, RISC, ARM, Systems on Chip (SoC)
3. Case Study (SoC): Qualcomm Snapdragon
4. GPU, GPGPU, NPU, VPU, TPU
5. IBM Power processor series
6. Sensors: Accelerometer, barometer, body temperature sensor, gyroscopic sensor, heart-beat sensor, magnetometer, proximity sensor
7. Case Studies: iPhone 8 and iPhone X
8. Network (Information) security
9. Mobile devices – Notebooks, iPhone, iPad
10. Wearable devices – smartbands, iWatch,
11. Virtual reality (VR), augmented reality (AR) – HTC Vive, Google Glasses, Microsoft HoloLens
12. Personal area networks – iPhone + NBs + Headset
13. Mobile ad hoc network (MANET) – Mobile sensor network (MSN), Vehicular ad hoc network (VANET).
14. Case Studies: Google Loon and Facebook Aquila
15. Cloud: Alibaba Cloud, Amazon Cloud Service, Google Cloud Platform, IBM Cloud, Microsoft Azure, SAP Cloud Platform, Oracle Cloud, Salesforce, Tencent Cloud, Dropbox, etc.
16. Internet of Things (IoT) Cyberphysical systems (Networking of customers, manufacturers (workers and machines), R&D teams, 3PL firms (workers, machines, trucks, vessels, planes), government agencies (officers and machines), and etc.)
17. Case Study: SAP Cloud Platform
18. Case Study: Google Inc.
19. 3D printing, flying robots
20. Case Study: Walmart Robot https://www.youtube.com/watch?v=_PErP8gGli4
21. Case Study: Carrot harvesting https://www.youtube.com/watch?v=xDsZC-s6V9g
22. Case Study: Rice box production https://www.youtube.com/watch?v=NT2FZ5PbdhI
23. Case Study: Automated BMW Car Factory https://www.youtube.com/watch?v=VpwkT2zV9H0 |
| 第5週 |
II. Evolution of Technologies: ICT
1. 4G LTE, 5G; WiFi, WiMax
2. Multicore CPU, RISC, ARM, Systems on Chip (SoC)
3. Case Study (SoC): Qualcomm Snapdragon
4. GPU, GPGPU, NPU, VPU, TPU
5. IBM Power processor series
6. Sensors: Accelerometer, barometer, body temperature sensor, gyroscopic sensor, heart-beat sensor, magnetometer, proximity sensor
7. Case Studies: iPhone 8 and iPhone X
8. Network (Information) security
9. Mobile devices – Notebooks, iPhone, iPad
10. Wearable devices – smartbands, iWatch,
11. Virtual reality (VR), augmented reality (AR) – HTC Vive, Google Glasses, Microsoft HoloLens
12. Personal area networks – iPhone + NBs + Headset
13. Mobile ad hoc network (MANET) – Mobile sensor network (MSN), Vehicular ad hoc network (VANET).
14. Case Studies: Google Loon and Facebook Aquila
15. Cloud: Alibaba Cloud, Amazon Cloud Service, Google Cloud Platform, IBM Cloud, Microsoft Azure, SAP Cloud Platform, Oracle Cloud, Salesforce, Tencent Cloud, Dropbox, etc.
16. Internet of Things (IoT) Cyberphysical systems (Networking of customers, manufacturers (workers and machines), R&D teams, 3PL firms (workers, machines, trucks, vessels, planes), government agencies (officers and machines), and etc.)
17. Case Study: SAP Cloud Platform
18. Case Study: Google Inc.
19. 3D printing, flying robots
20. Case Study: Walmart Robot https://www.youtube.com/watch?v=_PErP8gGli4
21. Case Study: Carrot harvesting https://www.youtube.com/watch?v=xDsZC-s6V9g
22. Case Study: Rice box production https://www.youtube.com/watch?v=NT2FZ5PbdhI
23. Case Study: Automated BMW Car Factory https://www.youtube.com/watch?v=VpwkT2zV9H0 |
| 第6週 |
III. AI and Machine Learning: Products and Projects
1. Intelligent agents (software robots)
2. Data mining, big data analytic and data science
3. Image/Face/Speech recognition
4. Case study: iPhone Siri
5. Case Study: Deep Learning in 11 Lines of MATLAB Code
https://www.youtube.com/watch?v=-ENmRfKWjmo
6. Video: Deep Learning Research and the Future of AI –
https://www.youtube.com/watch?v=5BrNt38OraE
7. Image/Language/Document/Video understanding
8. Recommender systems
9. Case studies: Amazon, Google
10. Image/Video tagging and captioning
11. Case Study: Facebook photo tagging, Google image tagging, Google Captioning Project
12. Case Study: Google Image Search, PlantSnap
13. Case Study: Google Translate, Apple QuickType
14. Sentiment analysis (Text, images, sound, video)
15. Social network analysis
16. Case Studies: IBM Deep Blue, AlphaGO
17. Case Study: IBM Watson
18. Machine vision, object recognition, target tracking, sentiment analysis
19. Case study: IBM Creates First Movie Trailer by AI for 20th Century FOX,
https://www.youtube.com/watch?v=gJEzuYynaiw
20. Autonomous vehicles
21. Case Study: Automated guided vehicles (AGV) https://www.youtube.com/watch?v=dAXdeqcHBp4 (Amazon Fulfillment Center), https://www.youtube.com/watch?v=UtBa9yVZBJM (Amazon Warehouse), https://www.youtube.com/watch?v=FBl4Y55V2Z4 (Alibaba warehouse robots).
22. Case Study: Autonomous trucks, Intelligent Autopilot System
23. Brain machine interface – Engineering brain-computer interfaces to regain control of movement https://www.youtube.com/watch?v=ZpTgdQEJc6I
24. Integrating Brain-Computer Interface Technology with Augmented and Virtual Reality. Paul Sajda, https://www.youtube.com/watch?v=fn9eBJFvSuA
25. Probabilistic processors
26. AI/NN Chips
Apple “Neural Engine” in A11 Bionic (should be for deep learning)
Apple “Image Signal Processing Unit” (ISP) in A11 Bionic
Huawei “Neural Processing Unit” (NPU) in Kirin 970 (deep learning)
Google on-device AI “Federated Learning”
Google “Tensor Processing Unit” (TPU)
IBM TrueNorth Chip (Spike NN, 1 million neurons and 256 million synapses)
Intel Movidius Myriad “Vision Processing Unit” (VPU)
Intel Nervana Neural Network Processor family (previous codename Lake Crest, deep learning)
Nvidia “Graphic Processing Unit” (GPU), deep learning accelerator. |
| 第7週 |
III. AI and Machine Learning: Products and Projects
1. Intelligent agents (software robots)
2. Data mining, big data analytic and data science
3. Image/Face/Speech recognition
4. Case study: iPhone Siri
5. Case Study: Deep Learning in 11 Lines of MATLAB Code
https://www.youtube.com/watch?v=-ENmRfKWjmo
6. Video: Deep Learning Research and the Future of AI –
https://www.youtube.com/watch?v=5BrNt38OraE
7. Image/Language/Document/Video understanding
8. Recommender systems
9. Case studies: Amazon, Google
10. Image/Video tagging and captioning
11. Case Study: Facebook photo tagging, Google image tagging, Google Captioning Project
12. Case Study: Google Image Search, PlantSnap
13. Case Study: Google Translate, Apple QuickType
14. Sentiment analysis (Text, images, sound, video)
15. Social network analysis
16. Case Studies: IBM Deep Blue, AlphaGO
17. Case Study: IBM Watson
18. Machine vision, object recognition, target tracking, sentiment analysis
19. Case study: IBM Creates First Movie Trailer by AI for 20th Century FOX,
https://www.youtube.com/watch?v=gJEzuYynaiw
20. Autonomous vehicles
21. Case Study: Automated guided vehicles (AGV) https://www.youtube.com/watch?v=dAXdeqcHBp4 (Amazon Fulfillment Center), https://www.youtube.com/watch?v=UtBa9yVZBJM (Amazon Warehouse), https://www.youtube.com/watch?v=FBl4Y55V2Z4 (Alibaba warehouse robots).
22. Case Study: Autonomous trucks, Intelligent Autopilot System
23. Brain machine interface – Engineering brain-computer interfaces to regain control of movement https://www.youtube.com/watch?v=ZpTgdQEJc6I
24. Integrating Brain-Computer Interface Technology with Augmented and Virtual Reality. Paul Sajda, https://www.youtube.com/watch?v=fn9eBJFvSuA
25. Probabilistic processors
26. AI/NN Chips
Apple “Neural Engine” in A11 Bionic (should be for deep learning)
Apple “Image Signal Processing Unit” (ISP) in A11 Bionic
Huawei “Neural Processing Unit” (NPU) in Kirin 970 (deep learning)
Google on-device AI “Federated Learning”
Google “Tensor Processing Unit” (TPU)
IBM TrueNorth Chip (Spike NN, 1 million neurons and 256 million synapses)
Intel Movidius Myriad “Vision Processing Unit” (VPU)
Intel Nervana Neural Network Processor family (previous codename Lake Crest, deep learning)
Nvidia “Graphic Processing Unit” (GPU), deep learning accelerator. |
| 第8週 |
III. AI and Machine Learning: Products and Projects
1. Intelligent agents (software robots)
2. Data mining, big data analytic and data science
3. Image/Face/Speech recognition
4. Case study: iPhone Siri
5. Case Study: Deep Learning in 11 Lines of MATLAB Code
https://www.youtube.com/watch?v=-ENmRfKWjmo
6. Video: Deep Learning Research and the Future of AI –
https://www.youtube.com/watch?v=5BrNt38OraE
7. Image/Language/Document/Video understanding
8. Recommender systems
9. Case studies: Amazon, Google
10. Image/Video tagging and captioning
11. Case Study: Facebook photo tagging, Google image tagging, Google Captioning Project
12. Case Study: Google Image Search, PlantSnap
13. Case Study: Google Translate, Apple QuickType
14. Sentiment analysis (Text, images, sound, video)
15. Social network analysis
16. Case Studies: IBM Deep Blue, AlphaGO
17. Case Study: IBM Watson
18. Machine vision, object recognition, target tracking, sentiment analysis
19. Case study: IBM Creates First Movie Trailer by AI for 20th Century FOX,
https://www.youtube.com/watch?v=gJEzuYynaiw
20. Autonomous vehicles
21. Case Study: Automated guided vehicles (AGV) https://www.youtube.com/watch?v=dAXdeqcHBp4 (Amazon Fulfillment Center), https://www.youtube.com/watch?v=UtBa9yVZBJM (Amazon Warehouse), https://www.youtube.com/watch?v=FBl4Y55V2Z4 (Alibaba warehouse robots).
22. Case Study: Autonomous trucks, Intelligent Autopilot System
23. Brain machine interface – Engineering brain-computer interfaces to regain control of movement https://www.youtube.com/watch?v=ZpTgdQEJc6I
24. Integrating Brain-Computer Interface Technology with Augmented and Virtual Reality. Paul Sajda, https://www.youtube.com/watch?v=fn9eBJFvSuA
25. Probabilistic processors
26. AI/NN Chips
Apple “Neural Engine” in A11 Bionic (should be for deep learning)
Apple “Image Signal Processing Unit” (ISP) in A11 Bionic
Huawei “Neural Processing Unit” (NPU) in Kirin 970 (deep learning)
Google on-device AI “Federated Learning”
Google “Tensor Processing Unit” (TPU)
IBM TrueNorth Chip (Spike NN, 1 million neurons and 256 million synapses)
Intel Movidius Myriad “Vision Processing Unit” (VPU)
Intel Nervana Neural Network Processor family (previous codename Lake Crest, deep learning)
Nvidia “Graphic Processing Unit” (GPU), deep learning accelerator. |
| 第9週 |
Mid-Term Exam/ Progress report presentation |
| 第10週 |
IV. AI & Machine Learning – Fundamental Concepts
Problems
Games
Object recognition
Language understanding
Automated control
Models for solving problems
Biological inspired
Neural networks
Spike neural networks
Rule-based
Expert systems
Fuzzy systems
Statistical Models
Structure equation models
Latent variables model
Generalized mixture models
Hybrid models
Extreme Learning Machine
Liquid State Machine
Convolution Neural Networks
Diagrams for the model structures
Performance measures
Win/Loss
Classification rate
Mean squared error (MSE)
Likelihood, etc.
Learning – Supervised, Unsupervised, Hybrid |
| 第11週 |
IV. AI & Machine Learning – Fundamental Concepts
Problems
Games
Object recognition
Language understanding
Automated control
Models for solving problems
Biological inspired
Neural networks
Spike neural networks
Rule-based
Expert systems
Fuzzy systems
Statistical Models
Structure equation models
Latent variables model
Generalized mixture models
Hybrid models
Extreme Learning Machine
Liquid State Machine
Convolution Neural Networks
Diagrams for the model structures
Performance measures
Win/Loss
Classification rate
Mean squared error (MSE)
Likelihood, etc.
Learning – Supervised, Unsupervised, Hybrid |
| 第12週 |
V. AI & Machine Learning – Neural Networks and Learning Systems
Fixed-Structure-and-Weights Networks
Hopfield Network, Brain-State-in-a-Box (BSB) Network,
Lagrange Program Neural Network, k-Winners-Take-All (kWTA)
Unsupervised Learning NN
Associative Network, Bidirection Associative Network (BAM)
Kohonen Map (Self-Organizing Map)
Supervised Learning NN
Multilayer Perceptron, Radial Basis Function (RBF) Network
Recurrent Neural Networks
Learning Systems
Gaussian Mixture Model, Independent Component Analysis (ICA)
Support Vector Machine (SVM), Boltzmann Machine
Restrictive Boltzmann Machine, Belief Network
Learning Objectives
Learning algorithms
Gradient descent, Newton Method, Gradient-Free Method
Simulated Annealing, Genetic Algorithm
Framework – NN/LS Model + Learning Objective + Learning Algorithm |
| 第13週 |
V. AI & Machine Learning – Neural Networks and Learning Systems
Fixed-Structure-and-Weights Networks
Hopfield Network, Brain-State-in-a-Box (BSB) Network,
Lagrange Program Neural Network, k-Winners-Take-All (kWTA)
Unsupervised Learning NN
Associative Network, Bidirection Associative Network (BAM)
Kohonen Map (Self-Organizing Map)
Supervised Learning NN
Multilayer Perceptron, Radial Basis Function (RBF) Network
Recurrent Neural Networks
Learning Systems
Gaussian Mixture Model, Independent Component Analysis (ICA)
Support Vector Machine (SVM), Boltzmann Machine
Restrictive Boltzmann Machine, Belief Network
Learning Objectives
Learning algorithms
Gradient descent, Newton Method, Gradient-Free Method
Simulated Annealing, Genetic Algorithm
Framework – NN/LS Model + Learning Objective + Learning Algorithm |
| 第14週 |
V. AI & Machine Learning – Neural Networks and Learning Systems
Fixed-Structure-and-Weights Networks
Hopfield Network, Brain-State-in-a-Box (BSB) Network,
Lagrange Program Neural Network, k-Winners-Take-All (kWTA)
Unsupervised Learning NN
Associative Network, Bidirection Associative Network (BAM)
Kohonen Map (Self-Organizing Map)
Supervised Learning NN
Multilayer Perceptron, Radial Basis Function (RBF) Network
Recurrent Neural Networks
Learning Systems
Gaussian Mixture Model, Independent Component Analysis (ICA)
Support Vector Machine (SVM), Boltzmann Machine
Restrictive Boltzmann Machine, Belief Network
Learning Objectives
Learning algorithms
Gradient descent, Newton Method, Gradient-Free Method
Simulated Annealing, Genetic Algorithm
Framework – NN/LS Model + Learning Objective + Learning Algorithm |
| 第15週 |
VI. AI & Machine Learning – Deep Learning
Models of deep neural networks
Deep Belief Network, Deep Boltzmann Machine
Autoencoder, Convolution Neural Network
Deep XYZ Network
Applications of deep neural networks
Image/Video Tagging, Sentiment Analysis
Voice recognition, Speech Recognition
Deep learning
Phase I: Layer-wise unsupervised learning
Phase II: Backpropagation with/without drop out or scaling mixture
Computation complexity
Accelerators
Apple “Neural Engine” in A11 Bionic
Huawei “Neural Processing Unit” (NPU) in Kirin 970
IBM TrueNorth Chip
Nvidia “Graphic Processing Unit” (GPU)
Limitations |
| 第16週 |
VI. AI & Machine Learning – Deep Learning
Models of deep neural networks
Deep Belief Network, Deep Boltzmann Machine
Autoencoder, Convolution Neural Network
Deep XYZ Network
Applications of deep neural networks
Image/Video Tagging, Sentiment Analysis
Voice recognition, Speech Recognition
Deep learning
Phase I: Layer-wise unsupervised learning
Phase II: Backpropagation with/without drop out or scaling mixture
Computation complexity
Accelerators
Apple “Neural Engine” in A11 Bionic
Huawei “Neural Processing Unit” (NPU) in Kirin 970
IBM TrueNorth Chip
Nvidia “Graphic Processing Unit” (GPU)
Limitations |
| 第17週 |
Final report presentation |
| 第18週 |
Final Exam/ Final report presentation |
|
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