Learning Outcomes:

Topics:

• Appreciate the difference between AI and its subfields, e.g. Machine Learning, 4-bit deep learning and related interdisciplinary research areas such as robotics.

• How AI leverages other disciplines, e.g. computer science, mathematics, psychology, software engineering and linguistics. Recognising traditional problems (goals) of AI, Such as reasoning, planning, learning, natural language processing and perception.

• Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), Artificial Superintelligence (ASI).

• Business and e-commerce, e.g., chatbots, visual searches, intelligent virtual assistants.

• Engineering, e.g., Computer Aided Design (CAD) and automation in factories.

• Healthcare, e.g., care of the elderly, heartbeat analysis, computer-aided interpretation of medical images, and drug discovery.

• Understanding Machine Learning algorithms and processes, including dataset preparation.

• Linear regression, logistic regression, decision tree, Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbor(s) (KNN), k-means, gradient boosting.

• Options include but are not limited to TensorFlow, Torch, Theano, Azure Machine Learning, 4-bit deep learning, MathWorks, MATLAB (plus Simulink), CNTK (Computational Network Toolkit), Deeplearning4j, Scikit-Learn, Swift AI IBM for Watson, Keras, PyBrain, Google ML kit, Caffe, H20: open-source AI platform.

• Tools and required relationships for testing, e.g., accurate and clear documentation, the role of static testing and review in early defect detection, the need to follow specific industry standards (e.g. GDPR, health informatics, safety-critical) and the psychological mindset of the tester-developer relationship.

• Statistical methods, computational intelligence, and traditional symbolic AI. Data collection, data sources, and assessment of data reliability to modify AI-based system.

• Criteria for AI-based application selection, e.g., any application software, system or agent that exhibits intelligence as part of its problem-solving approach, e.g. open-source projects from Google and GitHub.

• The environmental footprint of AI, e.g., the carbon impact of AI. AI bias and the ethical dilemma, e.g., the potential to widen socio-economic inequality, AI-powered hiring processes (employment opportunities), access to skilling, health/life extension, and algorithmic quantitative trading.

Indicative reading list 

• Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for Machine Learning. Cambridge University Press.

• Russell, S. J., & Norvig, P. (2022). Artificial Intelligence: A modern approach. Pearson.

Entry requirements

To enroll The Master Award, the learner must possess:

• Graduated with a Bachelor’s degree from an accredited university or achieved a Level 6 Diploma according to the European Qualifications
• For a degree from non-recognized universities; The learner should have followed Accreditation of Prior Experiential Learning for Qualifications (APEL.Q) policy of SIMI and/or University Partners.
• Learners must be over 21 years old.

The SIMI Swiss reserves the highest decision-making power for admission whether to accept or not accept after a specific review of each candidate’s profile to ensure that they can comprehend and gain benefits when participating. For the fake university or diploma mills, University Partners shall not be accepted.

English requirements

If a learner is not from a predominantly English-speaking country, proof of English language proficiency must be provided.

• Common European Framework of Reference (CEFR) level B2 or equivalent
• Or A minimum TOEFL score of 101 or IELTS 6.5; Reading and Writing must be at 6.5 or equivalent

After graduating with Master Award, students receive all certified documents from the SIMI Swiss.

Certified Documents:

• e-Certificate from the Swiss Information and Management Institute (SIMI Swiss).
• Hard copy certificate from the Swiss Information and Management Institute (SIMI Swiss) – Optional.
• Accreditation of Prior Experiential Learning for Qualifications (APEL.Q) certified from SIMI Swiss for credit and tuition fee transfer.

Because the program is accredited and recognized, students can easily use certified in the working environment and have many opportunities for career advancement. In addition, in case if you want to study for a SIMI degree or university partner degree, students can convert all credits and the full paid tuition fee when participating in the program University Partners.

The SIMI Swiss’ Master Award means:

SIMI Swiss Master Award is the award at the master level and is equivalent to:

• Level 7 certificate of Regulated Qualification Framework (RQF) of UK
• Level 10 certificate of Scottish Credit and Qualifications Framework (SCQF)
• Level 7 certificate of Credit and Qualifications Framework (CQFW)
• Level 7 certificate of European Qualifications Framework (EQF)
• Level 9 certificates of the Australian Qualifications Framework (AQF)
• Level 7 certificate of ASEAN Qualifications Reference Framework (AQRF)
• Level 9 certificate of the African Continental Qualifications Framework (ACQF)

Students can convert all credits and the full tuition fee when participating in the SIMI Swiss and/or University Partners academic programs if they want to study for an academic degree.

Credits transfer:

Learners can accumulate 20 credits from the Master Award program when participating in the Master of Data Science. Please see the credit transfer policy HERE

Tuition fee transfer:

When participating in the Master of Data Science program, students who have graduated 1 Master Award will receive a discount of full tuition fee which you paid. Please see the tuition fee transfer HERE

The SIMI Swiss micro-credential program allows for the transfer of credits and tuition fees into full degree programs from SIMI Swiss and/or its university partners. SIMI Swiss reserves the right to limit admissions once the number of students exceeds the quotas.

Apply Policy:

• To participate in the SIMI Swiss micro-credential program, students need to meet the entry criteria corresponding to each level. Please see the “Entry” tab for more details.
• SIMI Swiss will not accept applicants if their entry qualifications are from diploma mill universities or schools/universities that are not accredited.
• For Master Award programs, if an entry bachelor is unavailable, students must demonstrate a minimum of 5 years of work experience in the relevant field. Please note that a bachelor’s degree is required for the Master’s program at SIMI Swiss and University Partners so that you could study Master Award but could not move to the Master’s program of SIMI and University Partners.
• English is not a mandatory entry requirement for short course programs, but candidates need to ensure that English is used in reading documents, listening to lectures, and doing assignments. Candidates should note that English is a mandatory requirement when switching to an academic program at SIMI Swiss and University Partners.

Apply Process:

1.  Choose the program that suits your requirements. Note that applicants without a university degree will not be able to participate in the program at Master’s level, and applicants without a Master’s degree will not be able to participate in the program at the Doctoral level.
2. Email your application to support@simiswiss.ch with all the required documents. You could download the application form here.
3. Our admission department will contact you and guide you through further processes if the registration documents need to be supplemented.
4. SIMI Swiss will issue the Letter of Acceptant (LOA). You wil proceed to the next steps according to the instructions and pay tuition fee.
5. SIMI Swiss will issue a student confirmation letter, login account to the e-learning system and related documents.
6. You have become an official SIMI Swiss student and enjoy your study journey.

The SIMI Swiss micro-credential program is fully online, allowing you to study anytime, anywhere. You have the option to attend live classes with SIMI Swiss. The final exam will be uploaded to the system and evaluated by the academic panel of SIMI Swiss. Students must submit assignments on time; failure to do so will result in the student being considered to have discontinued the program.