Course Outcomes

1. Understand the basic theory and algorithms/ techniques that are widely used in digital image processing.
2. Understand image analysis algorithms.
3. Understand current applications of Image Processing.
4. Develop hands-on experience in using computers to process images.

Course Outcomes

1. Understand different complexity measures to analyze the complexity/performance of different algorithms.
2. Understand advanced techniques such as greedy algorithms, dynamic programming and know the concepts of tractable and intractable problems and the classes P, NP and NP-complete problems.
3. Understand and conduct mathematical proofs for computation and algorithms.

Course Outcomes

1. Develop hands-on experience in using computers to process images and solve various real world problems.
2. Develop skills using programming languages for realization and implementation of image processing problems.

Course Outcomes

1. Understand the fundamental of machine learning and deep learning methodologies with real-time examples.
2. Understand the fundamental of artificial neural networks with real-time examples.
3. Understand the basic theory and algorithms/ techniques that are widely used in digital image processing.
4. Understand image analysis algorithms.
5. Apply the definitions of the image classification and analysis problem to common problems in computer vision.
6. Understand current applications of Image Processing.
7. Develop hands-on experience in using computers to process images.
8. Design and execute an implementation of an image processing system using tools such as PyTorch or TensorFlow.

Course Outcomes

1. Understand different complexity measures to analyze the complexity/performance of different algorithms.
2. Understand advanced techniques such as greedy algorithms, dynamic programming and know the concepts of tractable and intractable problems and the classes P, NP and NP-complete problems.
3. Understand and conduct mathematical proofs for computation and algorithms.

Course Outcomes

1. Develop hands-on experience in using computers to process images and solve various real world problems.
2. Develop skills using programming languages for realization and implementation of image processing problems.

Course Outcomes

1. Understand the basics and fundamentals of statistical techniques commonly used in pattern recognition problem.
2. Understand the concepts of machine learning and deep learning techniques and various real world problems that can be handled by learning algorithms/ techniques.
3. Compare and contrast different pattern recognition algorithms.
4. Apply the concepts of machine learning and deep learning algorithms in real life problems.
5. Understand the concepts of decision making and modelling as a problem solving approach.

Course Outcomes

1. Understand the requirements and applications of each phase of compiler and various compilation techniques needed to obtain high performance on modern computer architectures.
2. Understand different optimization techniques and difference between them.
3. Understand formal language theory and its application to computer science.
4. Apply mathematical preliminaries to develop the basic components of language design.
5. Design simple computational machines using the concepts of language theory Correlate computability with formal computational machines.

Course Outcomes

1. Understand the various advanced topics in computer science and engineering for real-world applications.
2. Synthesize and integrate material from primary and secondary sources with their own ideas in research papers.

Course Outcomes

1. Solve certain real world practical problems in the domain of computer science and engineering and other interdisciplinary research areas.

Course Outcomes

1. Analyse and evaluate the importance of personal data its privacy and security.
2. Analyse and evaluate the security aspects of social media platforms and ethical aspects associated with use of social media.
3. Evaluate and communicate the human role in security systems with an emphasis on ethics, social engineering vulnerabilities and training.
4. Increase awareness about cyber-attack vectors and safety against cyber-frauds.
5. Take measures for self-cyber-protection as well as societal cyber-protection.

Course Outcomes

1. Understand the requirements and applications of each phase of compiler and various compilation techniques needed to obtain high performance on modern computer architectures.
2. Understand different optimization techniques and difference between them.
3. Understand formal language theory and its application to computer science.
4. Apply mathematical preliminaries to develop the basic components of language design.
5. Design simple computational machines using the concepts of language theory Correlate computability with formal computational machines.

Course Outcomes

1. Understand the various advanced topics in computer science and engineering for real-world applications.
2. Synthesize and integrate material from primary and secondary sources with their own ideas in research papers.

Course Outcomes

1. Solve certain real world practical problems in the domain of computer science and engineering and other interdisciplinary research areas.