Teaching and Learning
Teaching
Visiting Instructor/ Teaching Assistant - ENTC, University of Moratuwa
I assisted the lecturers in assignment preparation, evaluations, and laboratory work.
- BM4151 Biosignal Processing, UoM, Sri Lanka (Fall 2023)
- EN4553 Machine Vision, UoM, Sri Lanka (Fall 2023)
- BM3122 Medical Imaging, UoM, Sri Lanka (Fall 2023)
- EN3160 Image Processing and Machine Vision, UoM, Sri Lanka (Fall 2023)
- EN3551 Digital Signal Processing, UoM, Sri Lanka (Fall 2023)
- BM2012 Anatomy and Physiology for Engineers, UoM, Sri Lanka (Fall 2023)
Learning
Selected Undergraduate Modules
Up until the last semester as an undergraduate, I was involved in deep-learning research based on heuristic approaches. However, the ‘Pattern Recognition and Machine Intelligence’ module delivered by Dr. Prathapasinghe Dharmawansa made me realize the power and versatility of statistical machine learning and it catalyzed a passion to explore statistical approaches in medical image analysis.
Advanced Courses
- EN4573 Pattern Recognition and Machine Intelligence:
- Course content includes Multivariate Gaussian Density, Concentration of Measure in High Dimensions, Goals of Learning, Randomness of the Generalization Error, Inductive Bias, Density Estimation, Concentration Inequalities, Introduction to PAC Inequalities. (Reference materials are mostly from Statistical Learning Theory by Bruce Hajek and Justin Raginsky).
Mathematics
- MA4043 Neural Network and Fuzzy Logic
- MA4023 Operational Research
- MA4013 Linear Models and Multivariate Statistics
- MA3023 Numerical Methods
- MA3013 Applied Statistics
- MA2053 Graph Theory
- MA2033 Linear Algebra
- MA2023 Calculus
- MA2013 Differential Equations
- MA1023 Methods of Mathematics
Signal/ Image Processing
- BM4151 Biosignal Processing
- EN4553 Machine Vision
- BM3121 Medical Imaging
- EN2570 Digital Signal Processing
- EN2550 Fundamentals of Image Processing and Machine Vision
- EN2040 Random Signals and Processes
- EN1060 Signals and Systems
Biology
- BM4620 Biotechnology
- BM2101 Analysis of Physiological Systems
- BM2020 Human Anatomy and Physiology II
- BM2011 Human Anatomy and Physiology I
Computer Engineering
- EN3240 Embedded Systems Engineering
- EN3143 Electronic Control Systems
- EN3030 Circuits and System Design
- EN2030 Fundamentals of Computer Organization and Design
Electrical Engineering
- EN2083 Electromagnetics
- EE2093 Theory of Electricity
- EE1012 Electrical Engineering
General Engineering
- ME1822 Basic Engineering Thermodynamics
- EN1053 Introduction to Telecommunications
- MT1022 Properties of Materials
- CE1022 Fluid Mechanics
MOOCs
- TensorFlow Advanced Techniques : 4-Course Specialization - DeepLearning.AI (Coursera) March 2022
- Custom Models, Layers, and Loss Functions with TensorFlow
- Custom and Distributed Training with TensorFlow
- Advanced Computer Vision with TensorFlow
- Generative Deep Learning with TensorFlow
- MATLAB Programming for Engineers and Scientists : 3-Course Specialization - Vanderbilt University (Coursera) November 2020
- Introduction to Programming with MATLAB
- Mastering Programming with MATLAB
- Introduction to Data, Signal, and Image Analysis with MATLAB
- DeepLearning.AI Tensorflow Developer : 4-Course Professional Certificate - DeepLearning.AI (Coursera) September 2020
- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- Convolutional Neural Networks in TensorFlow
- Natural Language Processing in TensorFlow
- Sequences, Time Series and Prediction
- AI for Medical Diagnosis - DeepLearning.AI (Coursera) September 2020
- Python Classes and Inheritance - University of Michigan (Coursera) September 2020
- Anatomy : 4-Course Specialization - University of Michigan (Coursera) August 2020
- Anatomy: Musculoskeletal and Integumentary Systems
- Anatomy: Cardiovascular, Respiratory and Urinary Systems
- Anatomy: Human Neuroanatomy
- Anatomy: Gastrointestinal, Reproductive and Endocrine Systems
- Deep Learning : 5-Course Specialization - DeepLearning.AI (Coursera) June 2020
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
- Machine Learning - Stanford University (Coursera) June 2020