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IELTS, Higher Studies & Research — enrollment open. · View programme
Bootcamp · Beginner · Research, Machine Learning
A 6-month intensive research program taking students from research foundations and literature review to data collection, AI/ML development, proposed model implementation, rigorous evaluation, scientific writing, and publication-ready research.
৳50000
Installments available, 3 payments
A research-first, hands-on program designed to take learners from research foundations and literature review to dataset development, AI/ML experimentation, proposed model development, rigorous evaluation, scientific writing, and publication-ready research.
The program focuses on how research is actually conducted, from identifying a problem and reviewing literature to building, evaluating, writing, and preparing research for publication.
Every major stage combines learning, practical work, feedback, assignments, and a concrete research deliverable.
Learn how to turn a research problem into a structured and testable AI/ML study.
Build datasets, implement models, run experiments and analyze real results.
Each module produces an assignment, artifact, report, experiment or research document.
Learn how to move from a research gap to a proposed architecture and implementation.
Understand which metrics and validation strategies matter for different research problems.
Work toward a publication-ready manuscript through structured research and writing.
The hands-on methodology follows a complete research workflow. Students repeatedly apply the concepts through assignments, implementation and research deliverables.
Click any module to explore the topics, assignment and expected deliverables.
Students work through a structured research project that connects the modules into one complete workflow.
Define a focused problem, research questions, objectives, hypotheses and expected contribution.
Review previous work, compare methods and identify the research gap.
Collect or select data, perform preprocessing, annotation, quality control and leakage analysis.
Establish credible baseline models and define an appropriate experimental protocol.
Design and implement a proposed methodology addressing the identified research gap.
Conduct comparative experiments, ablations, statistical analysis and failure analysis.
Develop the methodology, results, discussion and other sections of a scientific manuscript.
Prepare the research package and identify suitable publication venues.
Model evaluation is not about reporting as many metrics as possible. Students learn how the research problem, class distribution, decision objective and experimental design determine the appropriate evaluation strategy.
Accuracy, Precision, Recall, F1 and confusion matrix can provide complementary evidence.
PR-AUC, Recall, F1, MCC and class-specific analysis may become more informative than accuracy alone.
ROC-AUC and PR-AUC help assess ranking and discrimination across thresholds.
MAE, MSE, RMSE and R² provide different views of prediction error and explained variance.
Calibration, confidence intervals and statistical analysis help assess reliability beyond a single score.
Cross-validation, external validation, error analysis and failure analysis help assess robustness.
Strong-performing students may have opportunities to continue their research journey beyond the classroom, subject to project quality, availability and AMIR Lab selection.
Selected students may have an opportunity to pursue research internship work with AMIR Lab based on research performance, project quality and available positions.
Selected high-quality research projects may have an opportunity to receive APC support from AMIR Lab, subject to research quality, target venue, publication suitability and AMIR Lab approval.
More than 40 alumni came through the 2025 batch. Many alumni went on to publish research papers after completing their research journey, reflecting the practical and research-focused direction of the program.
Program starts in January 2027. Early registration price is currently ৳40,000.
Start building the research skills, technical foundation and publication workflow needed for serious AI/ML research.
Instructors
Research & Publication Mentor · Professor, AIUB · 350+ publications · 10,000+ citations
Artificial Intelligence · Machine Learning · Computational Intelligence · Computer Vision · Large Language Models · NLP
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PhD Researcher · AI & Machine Learning Researcher · Higher Studies Mentor
Artificial Intelligence · Machine Learning · Deep Learning · Computer Vision · NLP · Robotics · Research
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