人工智能: 基础与应用课程 - 生产力学院
人工智能: 基础与应用课程
10011123-01
香港九龙达之路78号
2021-07-28
戴小姐 - 2788 5677
[email protected]

人工智能Artificial Intelligence (AI) 在工作上及日常生活中都变得越来越重要。这个课程适合技术人员和非技术人员参加,可令参加者于工作上更好的运用AI。本课程将介绍通用AI术语背后的含义,包括神经网络(neural networks),机器学习(machine learning),深度学习(deep learning)和数据科学(data science)。参与者还将学习到AI实际运用,以及如何应用AI解决工作及机构中问题。

(课程内容介绍以英文为准)

Objective

AI is getting more important not just in the workplace but also in daily life. If your organisation wants to become better at using AI, this is the course for both technical and non-technical colleagues. This course will introduce the meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science. You will also learn what AI realistically can and cannot do, and how to spot opportunities to apply AI to solve problems in your own organisation.

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Programme Highlights

  • The first and most essential course in the whole AI series
  • Understand the state-of-the-art Big Data Artificial Intelligence Technology
  • Learn the principles of AI algorithms from the ground up, basic statistics and basic machine learning methodology
  • AI Technology including ANN, RNN, LSTM, DQN
  • Understand how deep learning helps in image recognition, speech recognition, textual analytics, trend prediction, robotic control, self-driving, chess playing etc.

Who Should Attend?

  • For anyone who is interested in understanding the applications and principles of AI and strategies.
  • Project Managers and Management Executives, who intend to plan and utilise AI in their projects. They can learn what, why and how deep learning works in the projects.
  • Software Developers and System integrators, who can learn practically how algorithms and AI basics from the ground up.

Duration

Date : 28 & 29 Jul 2021 (Wednesday & Thursday)
Time: 09:30 – 17:00
Duration: 6 hours per day, total 12 lecture hours

Course Fee

HK$4,800

Venue

1/F, HKPC Building, 78 Tat Chee Avenue, Kowloon Tong

Programme Structure

Chapter 1 : Introduction to Big Data Analytics

  • What is Big Data Infrastructure
  • What is Big Data Analytics
  • Trends & History
  • Big Data 4Vs
  • Framework
  • Concepts of Unstructured Data

Chapter 2 : Introduction to Artificial Intelligence

  • Introduction to AI & History
  • Examples in Deep Learning
  • AI Applications in Computer Visions
  • AI Applications in Face and Gesture Recognition
  • AI Applications in Object Detection
  • AI Applications in Activity Recognition
  • AI Applications in Image Generation
  • AI Applications in Robotic Controls
  • AI Natural Language Processing
  • GPU and Hardware in AI

Chapter 3 : Introduction to Deep Learning

  • Global Deep Learning Trends
  • Machine Learning Process
  • Supervised & Unsupervised Learning
  • Reinforcement Learning
  • Classification Application Type
  • Regression Application Type
  • Clustering Application Type
  • Error Rate & Accuracy
  • Concepts of Feature Engineering
  • Revolution of Depth Layers

Chapter 4 : From Regression

  • Machine Learning Basics
  • Training, Test & Validation Process
  • Over-fitting & Under-fitting
  • From the Ground Up: Regression
  • Linear Regression
  • Logistic Regression
  • Principles in Perceptron
  • Impact of Data Cleansing and Data Transformation
  • Impact of Feature Engineering

Chapter 5 : Neural Network Basics, ANN, CNN

  • Neural Network Basics
  • Neurons
  • Activation Functions
  • Optimization & Loss
  • Activation Functions
  • Neural Network Layers
  • Artificial Neural Networks
  • Principles in Gradient Descent
  • Neural Network Weight Update Mechanisms
  • Basic Structures in Artificial Neural Network
  • 2D & 3D Convolution Operation
  • Convolutional Neurons
  • Feature Maps
  • Convolution Neural Networks
  • Word2Vec Embedding

Chapter 6 : Recurrent Neural Basics (RNN, LSTM)

  • Introduction to Recurrent Neural Network
  • Unrolling RNN Cells
  • Gates in Memory Cells
  • Memory Concepts in AI Models
  • Long-Short-Term-Memory Cells
  • Why GPU Hardware is significant
  • Applications in Speech Recognition

Chapter 7 : Recurrent Neural Network in Text Analytics

  • Textual Feature Engineering
  • Features: TF-IDF, N-grams
  • Application in Handwritten Sequence Generation
  • Application in Reading Comprehension
  • Application in Part-of-Speech Recognition
  • Application in Text Summarization
  • Application in Co-reference Resolution
  • Application in Time Series Analysis
  • Applications in Textual Analytics
  • Brief Introduction to BERT Models for AI Text

Chapter 8 : Reinforcement Learning (DQN)

  • Concepts in Reinforcement Learning
  • Principles in Deep-Q-Learning
  • Applications in Robotic Control
  • Applications in Game-Strategy Playing
  • Applications in Chess Playing
  • Applications in Self-Driving Car

Programme Instructor

Mr LEE Chi Man, Alan
Mr LEE held a senior management role in the technology division of an investment bank, overseeing the corporate strategy, product development and production management for more than 14 years. With extensive IT background, Mr LEE possesses practical project experience on sophisticated analytics and large-scale global technology project management. He also has rich training experience in the design and delivery of Big Data analytics, Fintech and Blockchain Technologies and Applications.

Certificate & Award

Participants who have completed 100% attendance will be awarded a certificate of attendance issued by the Hong Kong Productivity Council.