• The Minor in Analytics and AI will allow students to gain expertise in the current tools of AI analytics in engineering systems. Graduates with this minor will be equipped with both technical depth in their chosen discipline and exposure to the application of artificial intelligence techniques and algorithms to automate analysis processes, analyse and interpret data, derive insights, and make data-driven decision making.

Planning for Minor Study and Declaration

• The Analytics and AI minor consists of a collection of five (5) subjects.
• This minor is open only to non-ESD students matriculated from AY2022 onwards.
• The minor will be accorded upon successful completion of the required subjects by Term 8. No extension beyond Term 8 is allowed.
• Students interested in pursuing a minor or minors are required to meet an academic advisor to formulate a study plan. Students who are deemed to be unable to complete the coursework by Term 8 should not embark on a minor programme.
• Students will indicate their choice of Minor at the start of Term 8. Both the Major and Minor requirements must be completed within the normal candidature of the undergraduate programme, i.e. 8 terms.
• Subsequently, students are required to track their own progress towards completing the minor programme(s) as per the study plan formulated.
• Students must declare that they have successfully completed the minor programme(s) and provide any necessary documentation to OSA before graduation in order for the minor(s) to be reflected in their transcript.

Eligibility

• Students must have passed all twelve (12) Freshmore subjects in order to pursue a minor.

Curriculum Structure

• Pre-requisite: 10.022 Modelling Uncertainty
  Strongly recommended: 10.020 Data Driven World
• Mandatory:
  • 40.002 Optimisation
  • 40.016 The Analytics Edge or 50.038 Computational Data Science
  • 40.319 Statistical and Machine Learning or 50.007 Machine Learning
• ESD/ISTD/EPD Cross Pillar Electives: Choose up to twenty-four (24) credits from:
  • 30.100 Computational and Data-Driven Engineering
  • 30.114 Advanced Feedback & Control
  • 30.115 Digital Signal Processing
  • 40.316 Game Theory
  • 50.021 Artificial Intelligence
  • 50.039 Theory and Practice of Deep Learning
  • 50.035 Computer Vision
  • 50.040 Natural Language Processing
  • 50.043 Database Systems
  • 50.045 Information Retrieval
  • 50.050 Discrete Mathematics and Algorithm Design
  • 50.055 Special Topic: Machine Learning Operations

Grading Requirements

• In order to graduate with a minor, students are required to fulfil the conditions below by the end of Term 8. No extension beyond Term 8 is allowed.
  1. Successfully complete five (5) prescribed minor subjects which is the equivalent of sixty (60) subject credits.
  2. Successfully complete all core and designated subjects specified for the minor.
  3. Successfully complete all pillar and graduation requirements
• Subjects taken to complete a minor shall be graded and computed into the GPA. Unless a minimum passing grade is stated by the pillar, the minimum passing grade shall be a D. Non-letter grading options cannot be used for a minor. Grades earned towards the minor(s) shall still be computed into the GPA even if the student decides not to complete the minor programme(s).
• Credits from successfully completed academic units from external institutions can be recognized towards fulfilling SUTD minor requirements upon approval from the pillar/cluster offering the minor.