Why Build a Mobile App?
EXCLUSIVE
Be one of the first in the world to hold the exclusive dummies endorsed certificate.
FUN & ENRICHING
Learn how to make interactive Android apps in a fun, easy-to-understand way.
PROJECT-BASED
Create two functional apps for Android devices.
HIGH VALUE
Add mobile app development and an internationally recognised course to your portfolio with only one course.
First-of-its-kind Collaboration with
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FUN FACTS
Curriculum
Day 1
Start by learning the features of App Inventor, then use it to make a basic app with multiple screens.
Topics Covered:
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Programme Introduction
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Basic Mobile App Tools
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Project: Make an App About You
Day 2
Use your new skills to create a more complex app that allows you to add, edit and draw on pictures.
Topics Covered:
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Project: Make a Photo Editing App
Schedule
DECEMBER 2020
CLASS 1: 12 DEC & 13 DEC (FULL)
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Sat & Sun, 10am - 5pm
(Lunch break: 1pm - 2pm)
@ Online
CLASS 2: 28 DEC & 29 DEC
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Mon & Tue, 10am - 5pm
(Lunch break: 1pm - 2pm)
@ Online
Learning Made Easy
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INTRODUCTIONIntroduction What is AI good for?
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GRAPH-SEARCH ALGORITHMSBreadth-first search introduction Breadt-first search implementation Depth-first search introduction Depth-first search implementation I - with stack Depth-first search implementation II - with recursion Enhanced search algorithms introduction Iterative deepening depth-first search (IDDFS) A* search introduction
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BASIC SEARCH & OPTIMIZATION ALGORITHMSBrute-force search introduction Brute-force search example Stochastic search introduction Stochastic search example Hill climbing introduction Hill climbing example
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META-HEURISTIC OPTIMIZATION METHODSHeuristics VS meta-heuristics Tabu search introduction Simulated annealing introduction Simulated annealing - function extremum I Simulated annealing - function extremum II Simulated annealing - function extremum III Travelling salesman problem I - city Travelling salesman problem II - tour Travelling salesman problem III - annealing algorithm Travelling salesman problem IV - testing Genetic algorithms introduction - basics Genetic algorithms introduction - chromosomes Genetic algorithms introduction - crossover Genetic algorithms introduction - mutation Genetic algorithms introduction - the algorithm Genetic algorithm implementation I - individual Genetic algorithm implementation II - population Genetic algorithm implementation III - the algorithm Genetic algorithm implementation IV - testing Genetic algorithm implementation V - function optimum Swarm intelligence intoduction Partical swarm optimization introduction I - basics Partical swarm optimization introduction II - the algorithm Particle swarm optimization implementation I - particle Particle swarm optimization implementation II - initialize Particle swarm optimization implementation III - the algorithm Particle swarm optimization implementation IV - testing
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MINIMAX ALGORITHM - GAME ENGINESGame trees introduction Minimax algorithm introduction - basics Minimax algorithm introduction - the algorithm Minimax algorithm introduction - relation with tic-tac-toe Alpha-beta pruning introduction Alpha-beta pruning example Chess problem
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BUILDING TIC-TAC-TOEAbout the game Cell Constants and Player Game implementation I Game implementation II Board implementation I Board implementationj II - isWinning() Board implementation III Minimax algorithm Running tic-tac-toe
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INTERVIEW: SINGAPOREAN EXPERTBackground of Expert Information and Communication Technology in Singapore