Course Details
Topic 1: Introduction to Reinforcement Learning
- What is Reinforcement Learning (RL)?
- Markov Decision Process (MDP) and RL
- Applications of RL
- RL Algorithms Classifications
Topic 2: OpenAI Gym
- What is OpenAI Gym
- Install OpenAI Gym
- OpenAI Gym Operations
Topic 3: Value Based Q-Learning
- What is Q-Learning
- Q Value and Q-Table
- Bellman Equation
- Q-Learning Algorithm
- Epsilon Greedy Explore-Exploit Strategy
- On-Policy vs Off-Policy Learning
- What is SARSA?
- SARSA Algorithm
Topic 4: Policy-Based Learning
- Policy Based Methods
- Policy Gradient Algorithm
- Implementation of Policy Gradient Algorithm
Topic 5: Overview of Advanced RL Algorithms
- Limitation of Value and Policy-Based Learnings
- Actor-Critic Algorithms
- Deep Reinforcement Algorithms
Topic 6: Model-Based Learning
- What is Model-Based Learnings
- Model-Based Q-Learning Algorithms
Final Assessment
- Written Assessment - Short Answer Questions (WA-SAQ)
- Practical Performance (PP)
Course Info
Promotion Code
Promo or discount cannot be applied to WSQ courses
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
- Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Experience
- Minimum of 1 year of working experience.
- Minimum 18 years old
Minimum Software/Hardware Requirement
Software: NIL
Hardware: Windows and Mac Laptops
About Progressive Wage Model (PWM)
The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity.
Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.
For more information on PWM, please visit MOM site.
Funding Eligility Criteria
Individual Sponsored Trainee | Employer Sponsored Trainee |
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SkillsFuture Credit:
PSEA:
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Absentee Payroll (AP) Funding:
SFEC:
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Steps to Apply Skills Future Claim
- The staff will send you an invoice with the fee breakdown.
- Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
- Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
- Upload your invoice and click ‘Submit’
SkillsFuture Level-Up Program
The SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme
Get Additional Course Fee Support Up to $500 under UTAP
The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).
For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)
Steps to Apply UTAP
- Log in to your U Portal account to submit your UTAP application upon completion of the course.
Note
- SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
- All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
- An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
- UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
- UTAP support amount will be paid to training provider first and claimed after end of class by learner.
Appeal Process
- The candidate has the right to disagree with the assessment decision made by the assessor.
- When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
- If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
- If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
- If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
- If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
- The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
- The candidate must lodge the appeal within 7 days, giving reasons for appeal
- The assessor can help the candidate with writing and lodging the appeal.
- he assessment manager will collect information from the candidate & assessor and give a final decision.
- A record of the appeal and any subsequent actions and findings will be made.
- An Assessment Appeal Panel will be formed to review and give a decision.
- The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
- The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
- Please click the link below to fill up the Candidates Appeal Form.
Job Roles
- Machine Learning Engineer
- Robotics Engineer
- Game Developer (AI-focused)
- AI Research Scientist
- Data Scientist (branching into RL)
- Autonomous Systems Developer
- Simulation Engineer (using RL)
- Optimization Specialist
- AI Product Manager (oversight on RL projects)
- Control Systems Engineer (using RL)
- Finance Quant (using RL for trading strategies)
- NLP Engineer (using RL for certain applications)
- Recommendation System Developer (using RL)
- AI Solutions Architect
- Drone Algorithm Developer.
Trainers
Solomon Soh Zhe Hong: Solomon is ACTA certified and has trained and coached over 100 professionals in the area of data science, python programming and coding. Solomon is a Certified AI Engineer Associate by AI Singapore and holds certifications in Alibaba Cloud Architect and Alteryx respectively. Solomon interests include Reinforcement Learning, Natural Language Processing and Time-Series analysis.
Quah Chee Yong: Quah Chee Yong is a ACTA certified trainer. Quah Chee Yong Chee Yong is an experienced professional who has held various Technical, Operations and Commercial positions across several industriesA firm believer that AI can create a better world, he has equipped himself with the Knowledge and Skills in the fields of Data Science, Machine Learning, Deep Learning and Cloud Deployment
He has a deep passion for training & facilitating and is currently a Singapore WSQ certified Adult Educator. He particularly enjoys the interactive engagements with his fellow trainers and learners
Dr Alvin Ang: Dr Alvin Ang is a ACTA certified trainer. Alvin Ang did his Ph.D., Masters and Bachelors from NTU, Singapore. Previously he was a Principal Consultant (Data Science) as well as an Assistant Professor. He was also 8 years SUSS adjunct lecturer. His focus and interest is in the area of real world data science. Though an operational researcher by study, his passion for practical applications outweigh his academic background. He owns a startup externally
Customer Reviews (5)
- might recommend Review by Course Participant/Trainee
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1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - recommended Review by Course Participant/Trainee
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1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
I recorded the links I used to install the packages for scripts in an m1 env. Let me know if you need it
Bash files to install packages and environment (Posted on 7/31/2022) - will recommend Review by Course Participant/Trainee
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1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recommend Review by Course Participant/Trainee
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1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - will recomendation Review by Course Participant/Trainee
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1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
Good attempt for a difficult topic, keep it up! (Posted on 1/23/2022)