Approved Courses for Research Degree Students (MA)
Department of Mathematics
MPhil / PhD students are required to fulfill the stipulated core course and electives requirements.
Core Courses for PhD Students (9 credits) for 2019 cohort and thereafter:
| Code | Course Title | Credits |
|---|---|---|
| MA8011 | Methods for Applied Mathematics | 3 |
| AND Two course with 3 credits each to be selected from the following: |
||
| MA8004 | Selected Topics in Applied Analysis # | 3 |
| MA8005 | Advanced Partial Differential Equations I # | 3 |
| MA8006 | Functional Analysis and Applications # | 3 |
| MA8014 | Advanced Methods for Scientific Computation # | 3 |
| MA8019 | Topics in Statistical Machine Learning # | 3 |
# The course is included in the list of core courses for selection (MA8004, MA8005, MA8006, MA8014, MA8019) and the elective pool. Students cannot select the same course as a core course and an elective.
Core Courses for MPhil Students (3 Credits) for 2019 cohort and thereafter:
| Code | Course Title | Credits |
|---|---|---|
| MA8011 | Methods for Applied Mathematics | 3 |
Research Methodology and Ethics Courses for PhD Students (2 Credits)/MPhil Students (2 Credits) for 2019 cohort and thereafter:
| Code | Course Title | Credits |
|---|---|---|
| MA8003 | Research Methodology and Ethics | 2 |
Electives for PhD Students (3 Credits)/MPhil Students (3 Credits) for 2019 cohort and thereafter:
| Code | Course Title | Credits |
|---|---|---|
| MA8004 | Selected Topics in Applied Analysis | 3 |
| MA8005 | Advanced Partial Differential Equations I | 3 |
| MA8006 | Functional Analysis and Applications | 3 |
| MA8007 | Advanced Partial Differential Equations II | 3 |
| MA8008 | Complexity Theory | 3 |
| MA8014 | Advanced Methods for Scientific Computation | 3 |
| MA8015 | Advanced Learning Theory | 3 |
| MA8017 | Continuum Mechanics | 3 |
| MA8019 | Topics in Statistical Machine Learning | 3 |
| MA8020 | Selected Topics in PDEs | 3 |
| MA8021 | Topics in Applied and Computational Harmonic Analysis | 3 |
| MA8022 | Convex Optimization | 3 |
| MA8023 | Stochastic and Statistical Computation | 3 |
| MA8024 | Introduction to Kinetic Theory | 3 |
| MA8025 | Selected Topics in Random Matrix Theory | 3 |
| MA8026 | Advanced Mathematical Statistics | 3 |
| MA8027 | Linear Models | 3 |
| MA8028 | Machine Learning and Data Analysis | 3 |
Core Courses for PhD Students (7 credits) for 2018 cohort or before:
| Code | Course Title | Credits |
|---|---|---|
| MA8006 | Functional Analysis and Applications | 3 |
| AND One course with 3 credits to be selected from the following: |
||
| MA8004 | Selected Topics in Applied Analysis # | 3 |
| MA8005 | Advanced Partial Differential Equations I # | 3 |
| MA8014 | Advanced Methods for Scientific Computation # | 3 |
| MA8019 | Topics in Statistical Machine Learning # | 3 |
# These 4 courses can also be used as electives but students cannot select the same course as a core course and an elective.
Core Courses for MPhil Students (4 Credits) for 2018 cohort or before:
| Code | Course Title | Credits |
|---|---|---|
| MA8006 | Functional Analysis and Applications | 3 |
Electives for PhD Students (7 Credits)/MPhil Students (3 Credits) for 2018 cohort or before:
| Code | Course Title | Credits |
|---|---|---|
| MA8004 | Selected Topics in Applied Analysis | 3 |
| MA8005 | Advanced Partial Differential Equations I | 3 |
| MA8007 | Advanced Partial Differential Equations II | 3 |
| MA8008 | Complexity Theory | 3 |
| MA8014 | Advanced Methods for Scientific Computation | 3 |
| MA8015 | Advanced Learning Theory | 3 |
| MA8017 | Continuum Mechanics | 3 |
| MA8019 | Topics in Statistical Machine Learning | 3 |
| MA8020 | Selected Topics in PDEs | 3 |
| MA8021 | Topics in Applied and Computational Harmonic Analysis | 3 |
| MA8022 | Convex Optimization | 3 |
| MA8023 | Stochastic and Statistical Computation | 3 |
| MA8024 | Introduction to Kinetic Theory | 3 |
| MA8025 | Selected Topics in Random Matrix Theory | 3 |
| MA8026 | Advanced Mathematical Statistics | 3 |
| MA8027 | Linear Models | 3 |
| MA8028 | Machine Learning and Data Analysis | 3 |
