- Teacher: Prof. Asanga Ratnaweera
- Teacher: Prof C. S. Bandara
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Available courses
Automobile Engines
- Internal Combustion Engines
- Spark Ignition Engines: Gasoline Engines
- Compression Ignition Engines: Diesel Engines
- Hybrid Vehicles
- Lateral Dynamics and Braking
- Suspension Systems
- Steering Systems
- Transmission systems
- Teacher: Prof. Asanga Ratnaweera
- Teacher: Ms. L U Bakmeedeniya
- Teacher: Prof. S.D.G.S.P. Gunawardane
At the end of this course all students are expected to be able to qualitatively and quantitatively describe the motion of interconnected rigid body mechanisms.
- Teacher: Dr. D. H. S. Maithripala
- Teacher: Dr. Janaka Rajaguru
- Teacher: E/15/144 JAYARATHNA K.M.N.M. Ms.
- Teacher: Mr. Yapa Bandara
To introduce the fundamentals of vibrations theory and practice so that the students will be able to model and analyze
vibratory systems to estimate, measure, prevent and rectify vibrations in machines and structures.
- Teacher: Prof. Asanga Ratnaweera
- Teacher: Dr. D. H. S. Maithripala
- Teacher: Sachintha Kavishan
- Teacher: Roshan Ragel
- Teacher: Oshadhi Rajapakshe
This course familiarizes students with the principles and best practices necessary for the analysis, design, development, deployment, and maintenance of modern full stack software applications. Major design concepts and patterns are introduced then analyzed in the context of their implementations in modern full stack, front end, and back-end application development frameworks. Students put the concepts to practice by planning and executing a complete application development project in a team over the course of the semester. Students completing the course will be competent full stack application developers with the perspectives necessary to plan and execute application development projects in any enterprise.
This course requires either an undergraduate background in computer science, computer engineering, or information technology, or equivalent programming experience.
- Teacher: Somesh Rao Coka
- Teacher: Aparajita Biswal
- Teacher: Sonali Agarwal
This course enables the learners to understand the advanced concepts and algorithms
in machine learning. The course covers the standard and most popular supervised learning
algorithms such as linear regression, logistic regression, decision trees, Bayesian learning and the
Naive Bayes algorithm, basic clustering algorithms and classifier performance measures. This
course helps the students to provide machine learning based solutions to real world problems.
- Teacher: Sonali Agarwal
The course emphasizes on emerging data models and technologies suitable for managing different types and characteristics of data. Students will develop skills in analyzing, evaluating, modeling and developing database applications with concerns on both technical and business requirements.
- Teacher: teal2 ait
- Teacher: Anutariya Chutiporn
- Teacher: Sirikit Joshi
The course objective is to provide students hands-on programming skills and best practices related to Data Science and Artificial Intelligence. It is a laboratory course in which students will develop programming skills in loading, cleansing, transforming, modeling and visualizing data.
- Teacher: Sandhya Lamichhane
The course provides a foundation in the essential principles of machine learning and deep learning. Students will actively participate in assignments and hands-on projects, affording them practical exposure in developing machine learning-based applications. The predominant programming language employed throughout the course is Python. Prerequisites in programming experience and understanding of linear algebra will be helpful.
- Teacher: Chaklam Silpasuwanchai
- Teacher: Kavindu Herath
- Teacher: Sachintha Kavishan
- Teacher: Roshan Ragel
- Teacher: Oshadhi Rajapakshe
- Teacher: tealadmin User