About the program
Ongoing developments in information technologies are enabling the creation of information systems in a variety of fields, with an ever-increasing scale and sophistication. At the same time, users’ demands from information systems are also growing. Information system engineers are required to develop applications and products whose complexity and intricacy are constantly increasing. These systems utilize the latest technologies such as communication and distributed systems, command and control using artificial intelligence, data organization and retrieval, organizational resource management systems, e-commerce systems, integrated hardware and software systems and decision support systems.
The master’s program in Information Management Engineering includes research fields in system engineering and systems analysis, software engineering, software testing and verification, databases and data storage, artificial intelligence and autonomous systems, communication and distributed systems.
The program confers an M.Sc. in Information Management Engineering
Graduates of the program will be able to participate in academic and industrial research and development activities utilizing the knowledge and research skills developed during the course of the program. During research, graduate students will be able to discover new principles and methods that will enhance systems or form the basis for repurposed systems. Another option is an emphasis on development, where graduates will be able to create or improve infrastructure products for information systems or intelligent and complex information systems for organizations.
Admission requirements
Honors graduates with a B.Sc. in Information System Engineering from the Technion or other recognized universities will be admitted for studies. Candidates who have completed a B.Sc. in Computer Science, Data Science and Engineering, Mathematics or Industrial Engineering and Management who have completed their B.Sc. with honors may be required to take supplementary courses.
A minimum final average of 86 is required to apply.
Supplementary courses
Students are required to have a knowledge infrastructure in most of the following areas: basic information technologies, software engineering, algorithmics and operations research, artificial intelligence, communication, data mining, databases and cognitive sciences. Ideally, introduction to these fields takes place within Information System Engineering undergraduate studies. Students who are accepted into the program and have not studied the supplementary courses (or equivalent subjects) listed in the table below will be required to take these courses or their equivalents. Students with supplementary status must obtain a grade of at least 78 and an average of at least 80 in each of the supplementary subjects in order to transition to the status of a regular student. The student must complete all supplementary requirements within 2 semesters – repeating a supplementary course is not possible.
The list of supplementary courses will be determined according to the student’s academic background by the degree admissions committee.
iv. Examples of supplementary courses
| Course Number | Course Name | Pts. |
|---|---|---|
| 02340221 | Introduction to Computer Science N | 4 |
| 00940210 | Computer Architecture and Operating Sys. | 3.5 |
| 00940241 | Database Management | 3 |
| 00940223 | Data Structures and Algorithms | 4 |
| 00940424 | Statistics 1 | 3.5 |
| 00940314 | Stochastic Models in Oper.research | 3.5 |
| 00960224 | Distributed Data Management | 3.5 |
| 00960210 | Foundations and Applications of Artifici | 3.5 |
| 00960250 | Distributed Information Systems | 3.5 |
| 00960411 | Machine Learning 1 | 3.5 |
| 00950140 | Project Planning and Management | 3.5 |
| Course in Behavioral sciences |
Curriculum
To complete the degree, a graduate of a previous four-year degree must complete 20 credits from advanced degrees (+ 2 mandatory English credits) and a research paper as part of a thesis. A graduate of a three-year degree is required to complete 32 credits, of which up to 10 can be in advanced undergraduate subjects.
The collection of courses defined for the program reflects the research areas relevant to the field. The student must choose one subject from each of the following lists:
Please note!
Students who have completed 2 subjects from a list as part of their B.Sc. may be exempted from taking a course from the list, with the approval of the head of the program.
Each course can be taken into account under only one list.
The course lists will be updated from time to time.
Elective courses (to complete 22 credits) – according to the advisor’s authorization
| Course Number | Course Name | Pts. |
|---|---|---|
| 00960208 | Automatic Planning | 3.5 |
| 00970211 | Fault Tolerant Networks Protocols | 3.5 |
| 00970329 | Probablistic Algorithms | 2.5 |
| 00970280 | Algorithms in Uncertain Scenarios | 3 |
| 00960326 | Algorithms in Scheduling | 3.5 |
| 00960265 | Algorithms in Logic | 3 |
| 00970244 | Cognitive Robotics | 2.5 |
| 00970247 | The Internet of Things (iot) | 3 |
| Course Number | Course Name | Pts. |
|---|---|---|
| 00960415 | Topics in Regression | 3 |
| 00960425 | Time Series and Forecasting | 2.5 |
| 00970449 | Nonparametric Statistics | 2.5 |
| 00960450 | Multiple Comparisons | 2.5 |
| 00970414 | Statistics 2 | 3 |
| 00970470 | Semiparametric Models | 2 |
| 00980413 | Stochastic Processes | 3.5 |
| 00980414 | Theory of Statistics | 3 |
| 00980460 | Applied Multivariate Analysis | 3.5 |
| Course Number | Course Name | Pts. |
|---|---|---|
| 00960231 | Math Models in Advanced Info.retrieval | 3 |
| 00960293 | Machine Learning in Portfolio Selection | 2.5 |
| 00970200 | Deep Learning | 3.5 |
| 00970209 | Machine Learning 2 | 3.5 |
| 00970225 | Perturbation Methods in Machine Learning | 2.5 |
| 00960262 | Information Retrieval | 3.5 |
| 00960324 | Service Engineering | 3.5 |
| 00970135 | Multidisciplinary Research in Service | 3.5 |
| 00970215 | Methods in Natural Language Processing | 3 |
| 00970216 | Natural Language Processing | 2.5 |
| 00970248 | Machine Learning For Healthcare | 3 |
| 00970400 | Causal Inference | 2.5 |
| 00960212 | Probabilistic Graphical Models | 2 |
| Course Number | Course Name | Pts. |
|---|---|---|
| 00960327 | Nonlinear Models in Operations Research | 3.5 |
| 00960335 | Optimization Under Uncertainty | 3.5 |
| 00960336 | Optimization Methods in Machine Learning | 2 |
| 00960351 | Polyhedral Methods For Integer Prog | 2.5 |
| 00970334 | Algebriac Methods For Integer Progrmming | 2.5 |
| 00980311 | Optimization 1 | 3.5 |
| 00980312 | Optimization 2 | 3 |
| 00980331 | Linear and Combinatorial Programming | 3.5 |
| Course Number | Course Name | Pts. |
|---|---|---|
| 00960211 | Electronic Commerce Models | 3.5 |
| 00960226 | Computation | 2.5 |
| 00960291 | Algorithmic and High-frequency Trading | 2 |
| 00960501 | Economics For Systems Engineers | 3 |
| 00960578 | Social Choice and Preference Aggregation | 2.5 |
| 00960606 | Behavioral Econ. in Technological Env. | 3 |
| 00960690 | Behavioral Economics | 2.5 |
| 00970245 | Mechanism Design For Data Science | 2 |
| 00970246 | Social Computing Models | |
| 00970317 | Cooperative Game Theory | 2.5 |
| 00960292 | Predictive Analytics in Fintec | 3 |
| 00960586 | Econometrics | 3.5 |
| 00970510 | Continuous Time Models in Finance | |
| 00960693 | Psychological and Cognitive Networks | 3 |
| 00960694 | Metacognition | 2.5 |
| 00980292 | Creativity: Mind | 2 |
| 00970140 | Advanced Project Management Techniques | |
| 00960275 | The Human Factor in Data Collection | |
| 00960235 | Intelligent Interactive Systems[1] | |
| 00960617 | Cognition and Decision Making | 2.5 |
| 00970140 | Advanced Project Management Techniques | 3.5 |
| 00960625 | Cognition in Information Visualization | 3 |
[1] Can also be considered under data course
The course lists will be updated from time to time.
Elective courses (to complete 22 credits) – according to the advisor’s authorization
The student’s obligations
Completion of course requirements: All students are required to complete all course requirements.
Completing a thesis: The main part of the master’s degree is completion of a 20-credit research paper [2].
Submitting the thesis: Before completing the research, the student must present it in a field seminar paper (at least a month, but no more than a year before submission). The student must publish notice of the seminar according to the Technion’s rules in coordination with the seminar coordinator.
Graduate seminars: Each student must attend 10 seminars in fields relevant to their research. You must register for the mandatory course, Graduate Seminar 1 (00980409) 0 credits
According to the graduate school’s regulations, a 12-credit final paper can be authorized instead of a research paper or a research project. In those special cases, the student will be required to study additional courses with the permanent advisor’s authorization, of at least 8 credits. The thesis must be submitted approximately 24 months after the beginning of studies.
Additional regulations
An honors student during the master’s degree program may transfer to the direct track to a doctorate in accordance with the procedures of the graduate school.
The main part of the master’s degree program is completion of a 20-credit research paper. According to the graduate school’s regulations, a 12-credit final paper can be authorized instead of a research paper or a research project. In those special cases, the student will be required to study additional courses with the permanent advisor’s authorization, of at least 8 credits.
Students who wish to continue to doctoral studies will be required to comply with the graduate school’s procedures.
The requirements that apply to the student are those that were defined in the year in which they were accepted for studies; however, the faculty reserves the right to define additional scholastic requirements beyond those defined at the time of admission.