Ph.D. in Robotics and Autonomous System, March 2018 – Final Year

Centre for Robotics, Queensland University of Technology, Australian Centre for Robotic Vision, Australia

Ph.D. Topic: Failure Prediction Modeling for Robotic Vision

Achievements: Scientific paper published in IEEE/RSJ International Conference on Intelligent Robots and Systems 2019, Top Up scholarship from Australian Centre for Robotic Vision

Master of Science in Computer Science and Engineering Jan 2013 - Dec 2015

Department of Computer Science and Engineering, University of Dhaka, Bangladesh

Thesis Topic: A sliding window-based algorithm for detecting leaders from social network action streams

Achievements: Scientific paper published in IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 2015

Bachelor of Science in Computer Science and Engineering Mar 2008 - Jun 2012

Department of Computer Science and Engineering, University of Dhaka, Bangladesh

Thesis Topic: Stock market price prediction using artificial neural network.

Achievement: Acquired CGPA 3.51 out of 4.00. Participated in multiple inter/intra departmental programming contests.

Online Courses

Machine learning by Stanford University

  • Supervised and unsupervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks)

  • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)

Convolutional Neural networks

  • Applying CNN to visual detection and recognition tasks.

  • Approaches to use these algorithms to a variety of 2D or 3D data.

Structuring Machine Learning Projects

  • Understand how to diagnose errors in a machine learning system.

  • Know how to apply end-to-end transfer learning, and multi-task learning.

Neural Networks and Deep learning

  • How to build, train and apply fully connected deep neural networks

  • How to implement vectorized neural networks with parameters.

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

  • Understand industry best-practices for building deep learning applications.

  • Be able to implement a neural network in TensorFlow.

Applied Machine Learning in Python

  • Statistics behind machine learning algorithms.

  • Using tools (scikit learn, pandas) to solve machine learning problems.

Introduction to Data Science in Python

  • Introduces data manipulation and cleaning technique using python pandas data science library.

  • Introduce crucial data analysis techniques like inferential statistical analysis.