Machine Learning and AI

Perfect for beginners, non-coders and future coders, this program teaches students how machine intelligence can be used to solve real-world problems, allowing students to communicate with technical coders, design AI solutions, and be more prepared for the jobs in the workplace of the near-future.

Sign up here / Call Us (+852) 2850 4207

 

The world is changing, and changing rapidly. The advancement of technology is inevitable. How do you stay relevant?

By learning AI and machine learning–these skills are the skills of the future. A working knowledge of popular Machine Intelligence concepts will be one of the most in-demand skills for employees in the near-future workplace. Doctors, lawyers, bankers, and more will need to know how to identify problems which can be solved by AI, break them down, and communicate complex AI topics in familiar terms to their customers and technical employees, while developers must be able to build and apply AI solutions to problems with cutting edge tools such as Python algorithm code, Amazon Web Services, and more. This series of courses helps students prepare for the future by practicing these skills today, with a level right for everyone, whether they intend to learn how to code or not.

 

This course builds the foundation for utilizing AI and machine learning, for coders and non-coders alike, to solve real world problems. Courses are taught in a combination of lecture and in-class activities, with a large amount of interactive exercises, distributable illustrations, and optional readings, resources, and assignments to reinforce concepts discussed in class.

 

Three sections exist (101, 201 and 301). Each section builds upon one another and in the second and third, Python code is taught with each student coding along on their own laptop.

 

Section 101: Coding-Free Machine Intelligence

 

Additional Prerequisites: None

Learning Objectives: After this section, students will be able to

  • Recognize real-world problems where AI can add value
  • Categorize problems based on which algorithm approach is best
  • Break down problems and design algorithms to solve business problems
  • Understand and explain to non-technical users how popular machine intelligence solutions work
  • Be well prepared for internships in workplaces where humans must collaborate and communicate with AI

 

Section 201: Machine Intelligence In Python

Additional Prerequisites: A working laptop, and Section 101 or a demonstrated knowledge of basic statistics and a desire to learn how to code.

 

Learning Objectives: After this section, students will be able to

  • Write Python code
  • Automate manual tasks for data gathering and cleaning
  • Analyze data with machine learning and AI algorithms
  • Build AI models to:
    • Predict data such as inventory, stock prices, and sales
    • Identify users/customers based on their attributes
    • Find customers most similar to a given customer
    • Identify the contents of images
  • Visualize, interpret, and explain model results
  • Be well prepared for data analyst, business intelligence, or business analytics internships

 

Section 301: Applied Machine Intelligence

 

Additional Prerequisites: A working laptop and Section 201 or a demonstrated working knowledge of Python code and intermediate familiarity with AI concepts.

 

Learning Objectives: After this section, students will be able to:

  • Write advanced Python code
  • Be familiar with core features of Amazon Web Services
  • Build end-to-end automated solutions that run on cloud computers
  • Build advanced AI models and conduct advanced data analysis
  • Be well prepared for data-scientist, AI engineer, or project manager internships

 

We approach teaching as coaches instead of teachers.

 

Coaches:

Matt R O’Connor – former Lead Algorithmic Investment Engineer for Bridgewater Associates, currently Head of AI for Reboot.ai


Dhruv Sahi – former Lead Data Scientist for Grana, currently CEO Reboot.ai

 

 

Course advisor:

Chris Williams – New Colombo Plan Fellow, Bachelor of Computer Science, Data Science Major

https://www.linkedin.com/in/chris-williams-121929a0/

 

Schedule: Each section is 15 hours of total learning over 6 weeks, with a 2.5 hour class every Sunday for 6 weeks. Students who complete the full 3-part series will have 45 hours of total learning over a period of 18 weeks.

Audience: The courses in this progressive series are ideal for high school students.

Dates: Cohort 1: End of March – May 2019

Class size: 10 – 15

Fee for Section 101:  $9,750 per student per 15 hours. (Early bird: 8,700 per student per 15 hours)

**/