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Week - 1 |
Informing about internship procedures, department internship regulations, and occupational safety rules. |
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Week - 2 |
Examination of the organizational structure, working areas, and IT/Artificial Intelligence infrastructure of the host institution. |
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Week - 3 |
Adaptation to the professional working environment, code of ethics, and corporate workflows within the institution. |
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Week - 4 |
Definition and requirement analysis of the real-world problem/project to be studied during the internship. |
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Week - 5 |
Identification of data collection methods, data sources, and tools required for the project. |
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Week - 6 |
Examination of the collected raw data, exploratory data analysis (EDA), and cleaning processes for missing/noisy data. |
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Week - 7 |
Data preprocessing, normalization, and feature engineering studies for artificial intelligence and machine learning models. |
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Week - 8 |
Selection and design of appropriate machine learning or artificial intelligence algorithms/architectures for solving the problem. |
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Week - 9 |
Prototyping and coding of the selected algorithms in accordance with corporate standards and coding guidelines. |
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Week - 10 |
Training of the developed artificial intelligence models, hyperparameter optimization, and error analysis processes. |
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Week - 11 |
Testing and performance evaluation of the developed models according to industrial success metrics. |
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Week - 12 |
Interpretation of the results obtained, analysis of integration possibilities with corporate systems, and documentation. |
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Week - 13 |
Reporting of all technical works and achievements gained during the internship in accordance with academic standards. |
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Week - 14 |
Submission of the internship report (diary and technical report) to the internship committee and completion of evaluation processes. |