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AI for Junior

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Course start date: 29 November 2018
Number of lessons: 11
Total course hours: 30
Number of participants: 28

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Cost: CAD 14.99


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Artificial Intelligence (AI) and Machine Learning have made significant breakthroughs in the past decade. AI has deeply impacted our lives through its application in online search, mobile devices, social networks, and image analysis. The goal of this course is to provide a introduction to AI for high school students. This course will walk students through the definition of AI, the history of AI, AI algorithms, and the application of AI in real life situations. 

Target Audience:

1. High school students who want to learn AI before college.
2. College students who want to learn basic AI concepts and practice with real code before job.
3. Hobbyists

Lecture Schedule(EST):

  • Lecture can be attended in two ways (Live and Playback video)

  • Live Lecture schedule: 
  1. Lecture will be on live every Tuesday and Thursday at 8pm(EST).
  2. First lecture begins on Nov. 29th 2018 at 8pm(EST).
  3. Each lecture is about 2 hr.
  4. Two lectures a week.
  5. If any lecture schedule changes, a notification email will be sent to all enrolled users. 
  • PlayBack Lecture Video:
  1. Playback recording video will be available after each live lecture.
  2. Playback recording video can be viewed unlimited times within one year after enrolment.
  • Lecture Slides:
  1. Lecture slides will be available after each live lecture.


Owen Jian got his master of Applied Science in Quality System Engineering at Concordia University. After that, he has been focusing on Machine Learning and Deep Learning for years in industry, and obtains a strong knowledge and understanding about AI. His research mainly focuses on AI training model such as CNN, RNN and LSTM. Owen also has quite a lot software industry experience in Air Canada, Rogers. Currently he works as a software developer in Ceridian.

Isha Shingari was an Assistant Professor in both Institute of Innovation in Technology & Management of India and Jaipur National University of India. She is currently pursuing her PhD degree in Computer Science. Her search focuses on core expertise of Training model, data analysis and pre-processing of data. Establishing scalable, automated process for large scale data analyses, neural networks and model validation are also within her search.

Yixin Zhang, a PhD at University of Alberta, is also a Data Scientist and he is currently working at University of Alberta as well. Yixin works on large data analysis on oil sends, visualization and prediction on oil sands data for further processing. He also works on building probabilistic models of complex which utilizing Bayesian and monte Carlo methods.
AI Overview
  • Defining Artifical Intelligence
  • AI Background and History
  • AI Hardware
  • Comtemporary AI Influencers and their works
  • Challenges and Opportunities
  • AI Research
  • AI Application and Scenarios
  • AI Trends
  • Machine Learning and AI
  • Fundamental Concepts behind AI
  • AI Ecosystem and Terminology

Introduction to AI Systems

  • Image Recognition
  • Video Identification and Classification
  • Audio, Speech, and Translation ("Chatbot")
  • Sentiment Analysis
  • Music Maker
  • Self-driving Cars
  • Image and Video Inference
  • Reinforcement Learning
  • Gaming (Chess, Online Games)
  • Recommendation System
  • Knowledge Graphs
  • Blockchain - AI Tracking
  • Expert Decisions
  • Robots

Introduction to AI Skill Sets

  • Data Structures, Types, and Managment
  • Data Sample and Bias
  • Data Cleaning
  • Features and Extraction
  • Features Engineering and Transformation
  • Dimension Reduction
  • Classification and Regressino
  • Aritifical Neural Networks
  • Natural Language Processing
  • Deep Learning
  • Reinforcement Learning

  • Defining Artificial Intelligence
     The science and engineering of making intelligent machines, especially intelligent computer programs. Artifical intelligence is a way of making a computer, a computer-controlled robot, or a software to think intelligently, in similar manners to how humans think.

  • AI Background and History
     Enlightened from 'neural net'f proposed by Warren McCulloch and Walter Pitts in 1943

     In 1956, AI was founded as an academic discipline, followed by optimism of early AI between 1960 and 1970.

     Minsky wrote the book 'Perceptron'. Followed by two stages of 'AI Winter' in 1974 - 1980 (memory and speed constraints) and 1987 - 1993 (economic bubble) that saw significant reductions of AI funding.

     Rebranding as 'Deep Learning' (2006), Hinton introduced the idea of unsupervised pre-training and deep belief nets using restricted Boltzmann machine.

     The Breakthrough (2012), with Geoff Hinton's application of deep learning into Large Scale Visual Recognition Challenge (LSVRC), the error rate is reduced to 16%.

     Around 1995 to 1997, AI and Deep Learning began to gain popularity with the appearance of large, high-quality labeled datasets, along with the availability of high-performance GPUs.

Case 1:

Amazon's  recommender  system  can  identify  the  users  who  are  interested  in  purchasing  based  on  customer's  online  behavior.  That  is  why  Amazon  can  post  many  new  products  before  we  even  know  about  them.  The  interested  thing  is  that  when  we  browse  the  website  and  purchase  some  products,  the  recommended  products  are  seemingly  irrelevant  to  the  products  we  are  buying. 

Case 2:

Imagine  a  robot  with  AI  can  help  teachers  with  reviewing  knowledge  for  students  and  grading  the  exams?  With  AI,  the  knowledge  delivered  from  teachers  can  be  stored,  learned  and  even  processed  by  the  robot  student.  With  its  knowledge  refined  more  and  more  with  each  passing  year  while  sitting  together  with  real  students  listening  to  lectures,  the  robot  will  finally  develop  and  become  more  intelligent,  it  can  be  successfully  used  to  communicate  with  students  as  teaching  assistants.  At  the  same  time,  since  AI  is  able  to  store  astronomical  number  of  study  cases  and  students'  knowledge  level  and  scope,  it  can  quickly  organize  many  versions  quiz  and  exams  including  the  solutions.

Case 3:

If  you  are  not  sitting  in  a  driverless  car  for  traveling  every  day,  you  have  no  idea  what  you're  wasting  -  time!  Time  is  money!  Average  commuter  times  in  city  areas  is  around  30  minutes,  Image  almost  200  hours  saving  for  each  person,  how  much  dollar  value  can  be  created?  With  humans  no  longer  driving,  AI  in  driverless  car  is  likely  to  save  up  to  an  hour  every  day  –  time  that  will  undoubtedly  have  many  spin-off  benefits  from  wellbeing  to  boosting  the  economy.  What  is  more,  the  self-driving  cars  can  reduce  can  reduce  traffic  congestion  by  eliminating  stop-and-go  waves  caused  by  human  driving  behavior,  this  is  because  AI  enables  driverless  vehicles  to  communicate  with  each  other  and  their  surroundings,  hence  they  are  able  to  identify  the  optimal  route.  This  helps  spread  demand  for  scarce  road  and  speed.  The  more  important  benefit  of  such  AI-controlled  car  has  the  potential  in  the  future  to  reduce  deaths  and  injuries  from  car  crashes,  particularly  those  that  result  from  driver  distraction.

Case 4:

It  is  well  known  that  AI  is  now  applied  to  automatic  speech  recognition  (ASR):  speech-to-speech  and  voice-to-text  translation.  Many  translation  AI  products  is  to  get  this  to  95  percent  accuracy.  A  particular  example  is  the  Google's  Duplex,  a  new  technology  for  conducting  natural  conversations  to  carry  out  ͞real  world͟  tasks  over  the  phone.  For  instance,  you  wish  to  book  a  restaurant.  The  humans  on  the  other  end  of  the  line  were  actually  Duplex  that  handled  all  challenges  with  ease,  like  when  the  restaurant  said  it  has  vacancy  until  1  hour  after.  The  core  of  Duplex  is  a  recurrent  neural  network  (RNN)  designed  to  cope  with  speech  translation,  built  using  TensorFlow  Extended  (TFX).  To  obtain  its  high  precision,  the  AI  trains  Duplex͛s  RNN  on  a  corpus  of  anonymized  phone  conversation  data.  The  network  uses  the  output  of  Google͛s  

automatic  speech  recognition  (ASR)  technology,  as  well  as  features  from  the  audio,  the  history  of  the  conversation,  the  parameters  of  the  conversation  (e.g.  the  desired  service  for  an  appointment,  or  the  current  time  of  day)  and  more.


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