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| Last update: 2021/06/23 |
The Tables of Courses of Computer Science are as follows:
Table No. 1-1: Compulsory courses in logic and formal methods branches:
| No | Course | Units |
| 1 | Computational data mining | 3 |
| 2 | Advanced algorithms | 3 |
| 3 | Model checker | 3 |
Table No. 1-2: Specialized – optional courses of official languages branch and formal methods:
| No | Course | Units | Hours/ theory | Hours/applied | Total hours | Prerequisites or simultaneous courses |
| 1 | Model checker | 3 | 48 | 48 | ||
| 2 | Automatic proof | 3 | 48 | 48 | ||
| 3 | Logic programming | 3 | 48 | 48 | ||
| 4 | Formal semantics | 3 | 48 | 48 | ||
| 5 | Formal description of software | 3 | 48 | 48 | ||
| 6 | Software authentication | 3 | 48 | 48 | ||
| 7 | Special topics in formal methods | 3 | 48 | 48 | Lecturer permission |
The student is required to take at least 6 units of the courses in Table 2-1.
The student must take two of the courses in Tables 1-1 to7-1 or 1-2 to7-2 or one of the related master's degree courses according to the group advice.
Table No2-1: Compulsory courses in the field of scientific computing:
| No | Course | Units |
| 1 | Computational data | 3 |
| 2 | Advanced algorithms | 3 |
| 3 | Matrix computations | 3 |
Table 2-2: Specialized – optional courses of scientific computations.
| No | Course | Units | Hours / theory | Hours/ applied | Total hours | Prerequisites or simultaneous courses |
| 1 | Advanced math software | 3 | 48 | 48 | Numerical analysis 1 | |
| 2 | Linear numerical programming | 3 | 48 | 48 | Linear algebra | |
| 3 | Nonlinear-numerical optimization | 3 | 48 | 48 | Numerical linear algebra or numerical analysis 1 or matrix computations | |
| 4 | Advanced linear programming | 3 | 48 | 48 | Linear numerical programming or instructor permission | |
| 5 | Advanced nonlinear optimization | 3 | 48 | 48 | Numerical linear algebra or numerical analysis 1 or matrix calculations or group permission |
|
| 6 | Numerical Integral and differential equations | 3 | 48 | 48 | Numerical analysis 1 | |
| 7 | Numerical partial differential equations | 3 | 48 | 48 | Numerical analysis 1 | |
| 8 | Sparse matrices technology | 3 | 48 | 48 | Numerical linear algebra or matrix calculations or instructor permission | |
| 91 | Modeling and geometric design | 3 | 48 | 48 | Numerical linear algebra or matrix calculations or instructor permission | |
| 11 | Integer programing and networking | 3 | 48 | 48 | Numerical algebra, or numerical linear programming, or instructor permission | |
| 12 | Combinatory optimization | 3 | 48 | 48 | Numerical algebra, or numerical linear programming, or instructor permission | |
| 13 | Parallel algorithms for scientific computing | 3 | 48 | 48 | Numerical analysis 1 or instructor permission | |
| 14 | Numerical stochastic differential equations | 3 | 48 | 48 | Numerical analysis 1 or instructor permission | |
| 15 | Numerical stochastic partial differential equations | 3 | 48 | 48 | Ordinary stochastic differential equations, Simulation | |
| 16 | Simulation | 3 | 48 | 48 | Probability theory and stochastic processes, statistics | |
| 17 | Special topics in scientific computing |
3 | 48 | 48 | Instructor permission |
The student is required to take at least 6 units of the courses in Table 2-2.
The student must take two of the courses in Tables 1-1 to 7-1 or 1-2 to 7-2 or one of the related master's degree courses according to the group advice.
Table No. 3-1: Compulsory courses in Algorithm branch and theory of computations:
| No | Course | Units |
| 1 | Computational data | 3 |
| 2 | Advanced algorithms | 3 |
| 3 | Advanced theory of computation | 3 |
Table No. 3-2: Specialized-optional courses in computational theory:
| Course code | Course | Units | Hours/ theory | Hours/ applied | Total hours | Prerequisites or simultaneous courses |
| 1 | Recursion theory and computability | 3 | 48 | 48 | Instructor permission | |
| 2 | Computation complexity | 3 | 48 | 48 | ||
| 3 | Advanced computation complexity | 3 | 48 | 48 | ||
| 4 | Parallel algorithms | 3 | 48 | 48 | ||
| 5 | Stochastic algorithms | 3 | 48 | 48 | ||
| 6 | Design and analysis of algorithms | 3 | 48 | 48 | ||
| 7 | Fundamentals of cryptography theory | 3 | 48 | 48 | ||
| 8 | Games theory | 3 | 48 | 48 | ||
| 9 | Advanced graph theory | 3 | 48 | 48 | Graphs and algorithms | |
| 10 | Combinatorial algorithms | 3 | 48 | 48 | ||
| 11 | Graphs and algorithms | 3 | 48 | 48 | ||
| 12 | Approximate algorithms | 3 | 48 | 48 | ||
| 13 | Computational geometry | 3 | 48 | 48 | ||
| 14 | combinatorics | 3 | 48 | 48 | Combinatorial analysis 1 | |
| 15 | Structural compounds | 3 | 48 | 48 | ||
| 16 | Computational analysis | 3 | 48 | 48 | Mathematical logic, Mathematical analysis | |
| 17 | Special Topics in computational theory | 3 | 48 | 48 | Instructor permission |
The student is must take at least 6 units of the courses from Table 2-3
The student must take two of the courses in Tables 1-1 to 7-1 or 2-1 to 7-2 or one of the related master's degree courses, depending on the group advice.
Table 4-1: Compulsory courses in soft computing and artificial intelligence.
| No | Course | Units |
| 1 | Computational data | 3 |
| 2 | Advanced algorithms | 3 |
| 3 | Advanced artificial intelligence 3 | 3 |
Table No. 4-2: Specialized- optional courses - Soft computing and artificial intelligence:
| Course code | Course | Units | Hours/ theory | Hours/applied | Total hours | Prerequisites or simultaneous courses |
| 1 | Soft computing | 3 | 48 | 48 | - | |
| 2 | Advanced artificial intelligence | 3 | 48 | 48 | - | |
| 3 | Expert systems | 3 | 48 | 48 | - | |
| 4 | Machine learning | 3 | 48 | 48 | - | |
| 5 | Natural languages processing | 3 | 48 | 48 | - | |
| 6 | Statistical Machine learning | 3 | 48 | 48 | Machine learning | |
| 7 | Discrete dynamic systems | 3 | 48 | 48 | - | |
| 8 | Intelligent algorithms | 3 | 48 | 48 | - | |
| 9 | Multi agent systems | 3 | 48 | 48 | - | |
| 10 | Deep learning | 3 | 48 | 48 | Machine learning | |
| 11 | Data mining | 3 | 48 | 48 | - | |
| 12 | Advanced network optimization | 3 | 48 | 48 | - | |
| 13 | Special Topics in Artificial Intelligence | 3 | 48 | 48 | Instructor permission | |
| 14 | Special Topics in Soft Computing | 3 | 48 | 48 | Instructor permission |
The student is required to take at least 6 units of the courses in Table 4-2.
The student must take two of the courses in Tables 1-1to 7-1 or 1-2 to 7-1, or one of the related master's degree courses according to the group advice.
Table 5-1: Compulsory courses in systems theory:
| No | Course | Units |
| 1 | Computational data mining | 3 |
| 2 | Advanced algorithms | 3 |
| 3 | Advanced software design | 3 |
Table No. 5-2: Specialized-optional courses in systems theory:
| Course code | Course | Units | Hours/theory | Hours/ applied | Total hours | Prerequisites or simultaneous courses |
| 1 | Advanced software design | 3 | 48 | 48 | - | |
| 2 | Advanced agent system | 3 | 48 | 48 | - | |
| 3 | Advanced data base | 3 | 48 | 48 | - | |
| 4 | Real time systems | 3 | 48 | 48 | - | |
| 5 | Decision support systems | 3 | 48 | 48 | - | |
| 6 | Advanced compiler | 3 | 48 | 48 | - | |
| 7 | Distributed systems | 3 | 48 | 48 | Artificial intelligence | |
| 8 | Advanced computer networks | 3 | 48 | 48 | - | |
| 9 | Special Topics in Systems Theory | 3 | 48 | 48 | Instructor permission |
The student is required to take at least 6 units of the courses in Table 5-2. advice.
Table No. 6-1: Compulsory courses in Decision Science and Knowledge:
| No | Course | Units |
| 1 | Computational data mining | 3 |
| 2 | Advanced algorithms | 3 |
| 3 | Convex optimization |
3 |
Table No 6-2: Specialized-optional Courses in Decision Science and Knowledge:
| Course code | Course | Units | Hours/theory | Hours/applied | Total hours | Prerequisites or simultaneous courses |
| 1 | Decision with multiple criteria | 3 | 48 | 48 | Operations Research | |
| 2 | Soft computing | 3 | 48 | 48 | - | |
| 3 | Machine learning | 3 | 48 | 48 | - | |
| 4 | Information and uncertainty | 3 | 48 | 48 | - | |
| 5 | Fuzzy decision systems | 3 | 48 | 48 | Decision with multiple criteria | |
| 6 | Learning mathematics | 3 | 48 | 48 | - | |
| 7 | Combinatorial optimization | 3 | 48 | 48 | - | |
| 8 | Stochastic processes | 3 | 48 | 48 | - | |
| 9 | Probability and fuzzy statistics | 3 | 48 | 48 | Soft computing | |
| 10 | Games theory | 3 | 48 | 48 | - | |
| 11 | Transcendental optimization | 3 | 48 | 48 | Operational research | |
| 12 | Data mining | 3 | 48 | 48 | - | |
| 13 | Advanced data mining | 3 | 48 | 48 | Data mining | |
| 14 | Text mining & web mining | 3 | 48 | 48 | Data mining | |
| 15 | Artificial Neural Networks | 3 | 48 | 48 | Mathematical optimization or Instructor permission | |
| 16 | Multi agent systems | 3 | 48 | 48 | - | |
| 17 | Special topics in decision science and knowledge | 3 | 48 | 48 | Instructor permission |
The student is required to take at least 6 units of the courses in Table 2-6.
The student must take two of the courses in Tables 1-1 to 7-1 or 1-2to 7-1, or one of the related master's degree courses, depending on the group permission.
Table 7-1: Compulsory courses of Data Mining branch:
| No | Course | units |
| 1 | Computational data mining | 3 |
| 2 | Advanced algorithms | 3 |
| 3 | Data mining | 3 |
Table 7-2: Specialized-optional courses in data mining branch:
| Course code | Course | Units | Hours/ theory | Hours/applied | Total hours | Prerequisites or simultaneous courses |
| 1 | Learning mathematics | 3 | 48 | 48 | - | |
| 2 | Convex optimization | 3 | 48 | 48 | - | |
| 3 | Combinatorial optimization | 3 | 48 | 48 | - | |
| 4 | Machine learning | 3 | 48 | 48 | - | |
| 5 | Statistical machine learning | 3 | 48 | 48 | Machine learning | |
| 6 | Advanced data mining | 3 | 48 | 48 | Data mining | |
| 7 | Text mining and web mining |
3 | 48 | 48 | Data mining | |
| 8 | Feature selection and feature extraction | 3 | 48 | 48 | Data mining or Instructor permission | |
| 9 | Graph mining | 3 | 48 | 48 | Data mining or Instructor permission | |
| 10 | Probabilistic graph models | 3 | 48 | 48 | Data mining or Instructor permission | |
| 11 | Complex Networks | 3 | 48 | 48 | Data mining or Instructor permission | |
| 12 | Data visualization | 3 | 48 | 48 | Data mining or Instructor permission | |
| 13 | Outlier detection | 3 | 48 | 48 | Data mining or Instructor permission | |
| 14 | Modeling and data processing | 3 | 48 | 48 | Data mining | |
| 15 | Deep learning | 3 | 48 | 48 | Machine learning | |
| 16 | Special topics in data mining | 3 | 48 | 48 | Instructor decision |
The student is required to take at least 6 units of the courses in Table 7-2.
The student must take two of the courses in Tables 1-1to 7-1 or 1-2to 7-2, or one of the related master's degree courses, depending on the group permission.
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