Synopsis
Topics
- Introduction to Machine Learning
- Introduction to Deep Learning and Neural Networks
- Introduction to Python and Pytorch
- Hands on Deep Learning Practice
- Convolutional Neural Networks
- Natural Language Processing
- Explainable Machine Learning
- Recommender System
- Ethics of Artificial Intelligence
- Generative Models
- Deep Learning Applications for Business
- Data Visualization and Analysis
Learning Outcome
- Distinguish between machine learning and deep learning
- Construct suitable deep learning models for business applications
- Assess the trends of deep learning and artificial intelligence
- Recommend deep learning models to solve business problems
- Prepare high-dimensional datasets by Python and Pytorch
- Evaluate the result of deep learning models
Who Should Attend
Managers and executives engaged in leveraging advanced Artificial intelligence (AI) and Machine learning(ML) methods for data-driven decision-making to address business challenges.
Relevance of Course to employment/upskilling/reskilling
The course helps student build key competencies by imparting essential knowledge of deep learning models like Convolutional neural networks (CNNs), Natural language processing (NLP), recommender systems, and generative models. It equips individuals to construct deep learning models for practical business applications. Its industry relevance lies in addressing current trends and ethics in deep learning, ensuring graduates can develop and implement these models effectively to solve real-world business challenges. The course fosters skills in recommending deep learning solutions, data preparation using Python and PyTorch, and model evaluation, making graduates valuable assets in an Artificial intelligence (AI)-driven job market.
Admissions pre-requisite
- An undergraduate degree or an equivalent qualification from a recognised institution
- Specific courses may have additional requirements or pre-requisite (e.g. counselling courses). For more information, you may contact the Head of Programme regarding additional course requirements or pre-requisite
Subject to your eligibility and the approval of the Head of Programme, credits earned (up to a cap of 30 credit units) from the completion of Graduate CET Modular courses from the suite of Graduate Programmes may be recognised when admitted to the relevant Graduate Programmes.
Schedule
| Time | Agenda |
|---|---|
| Week 1 | |
| 19:00 | Course Overview |
| 19:15 | Introduction to AI and Deep Learning |
| 20:30 | Break |
| 20:45 | Introduction to Deep Learning Libraries and Hands-on Activities |
| 22:00 | Assignments |
| Week 2 | |
| 19:00 | Course Overview |
| 19:15 | Introduction to Torch Module |
| 20:30 | Break |
| 20:45 | Introduction to Sequential Models and Hands-on Activities |
| 22:00 | Assignments |
| Week 3 | |
| 19:00 | Course Overview |
| 19:15 | Introduction to Image Classification |
| 20:30 | Break |
| 20:45 | Introduction to CNNs and Hands-on Activities |
| 22:00 | Assignments |
| Week 4 | |
| 19:00 | Course Overview |
| 19:45 | Introduction to NLPs |
| 20:30 | Break |
| 20:45 | Introduction to Recurrent Neural Networks (RNNs) and Hands-on Activities |
| 22:00 | Assignments |
| Week 5 | |
| 19:00 | Course Overview |
| 19:15 | Introduction to Generative AI |
| 20:30 | Break |
| 20:45 | Introduction to Large Language Models (LLMs) and Hands-on Activities |
| 22:00 | Assignments |
| Week 6 | |
| 19:00 | Course Overview |
| 19:15 | Introduction to Recommendation System |
| 20:30 | Break |
| 20:45 | Introduction to Explainable AI |
| 22:00 | Assignments |
Assessments
The overall course grade is determined by
- Others, Assignments
- Quiz, Case study
Trainer info
Course Completion requirements
- Participants are required to achieve at least 75% attendance and pass any prescribed examinations/assessments or submit any course/project work (if any) under the course requirement.
- Participants are required to complete all surveys and feedbacks related to the course
- The course fees are reviewed annually and may be revised. The University reserves the right to adjust the course fees without prior notice.
- Singapore University of Social Sciences reserves the right to amend and/or revise the above schedule without prior notice
Course Fees, payment and refund policy
| International Participants | Singapore Citizens (below 40yrs), Permanent Residents | Singapore Citizens (40yrs and above) SkillsFuture Mid - Career Enhanced Subsidy1 | Enhanced Training Support for SMEs2 (Singaporean and PRs) | |
|---|---|---|---|---|
| Course Fees (A) | $3,168.00 | $2,640.00 | $2,640.00 | $2,640.00 |
| SSG Grant (70%) (B) | $1,848.00 | $1,848.00 | $1,848.00 | |
| Nett Course fees (A) - (B) = (C) | $3,168.00 | $792.00 | $792.00 | $792.00 |
| 9% GST on Nett course fees (D) | $285.12 | $71.28 | $71.28 | $71.28 |
| Total nett course fees payable including GST (C) + (D) | $3,453,12 | $863.28 | $863.28 | $863.28 |
| Less additional funding if eligible under various schemes (F) | - | - | $528.00 | $528.00 |
| Total nett course fees payable including GST, after additional funding form the various schemes (E) - (f) = (H) | $3,453.12 | $863.28 | $335.28 | $335.28 |
For the various payment mode, please refer here.
For the refund policy, please refer here.
For clarification, please contact the SUSS Academy via the following:
Telephone: +65 6248 0263
Email: [email protected]