MTech 3rd Semester Syllabus (2024)

Level up your MTech journey! This guide unlocks the MTech 3rd Semester Syllabus (2024), to offer a roadmap for the next leg of your academic adventure. Now that you have built the foundation of your first two semesters, you’ll dive deeper into the specialized subjects and ignite your passion for your chosen engineering field. So, buckle up, embrace the learning curve, and let’s conquer this semester together!

MTech CSE Sem 3 Syllabus

Subject	Unit-wise Syllabus	Books
Machine Learning	Unit 1: Introduction to Machine Learning: Supervised learning: Regression (linear, logistic, etc.), classification (decision trees, support vector machines, etc.) Unsupervised learning: Clustering (k-means, hierarchical), dimensionality reduction (PCA) Performance evaluation metrics (accuracy, precision, recall, etc.) Unit 2: Advanced Machine Learning Models: Neural networks (perceptrons, multi-layer perceptrons, convolutional neural networks, recurrent neural networks) Ensemble methods (random forests, gradient boosting) Deep learning architectures and applications Unit 3: Model Selection, Optimization, and Evaluation: Feature selection and engineering techniques Model hyperparameter tuning and optimization methods Cross-validation and other evaluation strategies Overfitting and underfitting prevention strategies Unit 4: Machine Learning Applications and Ethics: Case studies in various domains (e.g., natural language processing, computer vision, recommender systems) Bias and fairness considerations in machine learning Explainable AI and interpretability techniques”	“Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow” by Aurélien Géron “Deep Learning” by Ian Goodfellow, Yoshua Bengio, & Aaron Courville “Machine Learning: A Probabilistic Perspective” by Kevin P. Murphy”
Artificial Intelligence	Unit 1: Search and Reasoning: Uninformed and informed search algorithms (breadth-first, depth-first, A*, heuristic search) Knowledge representation and reasoning techniques (propositional logic, first-order logic) Planning and decision-making under uncertainty Unit 2: Advanced AI Techniques: Game playing and reinforcement learning Natural language processing (NLP) fundamentals (text classification, sentiment analysis) Introduction to computer vision (image recognition, object detection) Unit 3: Multi-agent Systems and Robotics: Agent communication and cooperation Multi-agent learning and planning Introduction to robot motion planning and control Unit 4: Philosophical and Ethical Issues in AI: The Turing Test and consciousness Machine morality and ethical decision-making Societal impacts of AI and potential risks”	“Artificial Intelligence: A Modern Approach” by Stuart Russell & Peter Norvig “Reinforcement Learning: An Introduction” by Richard S. Sutton & Andrew G. Barto “Speech and Language Processing” by Dan Jurafsky & James H. Martin
Distributed Systems	Unit 1: Introduction to Distributed Systems: Distributed computing concepts and challenges Client-server architectures, peer-to-peer networks, and distributed databases Concurrency control and distributed transactions Unit 2: Communication and Coordination in Distributed Systems: Remote procedure calls (RPC), message passing, and distributed communication protocols Distributed consensus algorithms (Paxos, Raft) Clock synchronization and consistency models Unit 3: Fault Tolerance and Scalability: Replication and fault tolerance mechanisms Load balancing and distributed task scheduling Distributed file systems and distributed storage Unit 4: Distributed Systems Applications and Security: Case studies in various applications (e.g., cloud computing, blockchain) Security challenges and solutions in distributed systems Distributed system monitoring and management”	“Distributed Systems: Concepts and Design” by George Coulouris, Jean Dollimore, & Tim Kindberg “Designing Data-Intensive Applications” by Martin Kleppmann “Distributed Systems for Fun and Profit” by Martin Kleppmann

Remember, that active participation, seeking guidance when needed, and utilizing available resources are very crucial to master the curriculum. By strategically approaching your studies you can ensure a successful and enriching third semester experience. So, embrace the challenges, explore the possibilities, and pave the way for a rewarding MTech experience.

FAQs

1. What practical projects or labs are part of the MTech 3rd Semester Syllabus for 2024?

The syllabus often includes hands-on projects related to Cybersecurity, Big Data Analytics, and Robotics to provide practical experience to students.

2. Are there any prerequisites or recommended background knowledge required for the courses in the MTech 3rd Semester Syllabus for 2024?

Some courses may have prerequisites, such as prior knowledge in programming languages, algorithms, or specific mathematical concepts, which are usually mentioned in the course descriptions.

3. How can students best prepare for the courses included in the MTech 3rd Semester Syllabus for 2024?

To prepare effectively, students can review relevant textbooks, online resources, and participate in study groups or discussions with peers to deepen their understanding of the subject matter.

4. Are there any industry collaborations or guest lectures integrated into the MTech 3rd Semester Syllabus for 2024?

Many universities often invite industry experts for guest lectures, workshops, or collaborative projects to provide students with real-world insights and networking opportunities.

5. What career opportunities can students expect after completing the MTech 3rd Semester Syllabus for 2024?

Graduates can pursue careers in various fields such as Data Science, Artificial Intelligence, Software Development, Cybersecurity, and Research & Development, depending on their specialization and interests.