Program Details

M.Tech Program Details¶

Program Information¶

Degree: WILP Master of Technology (M.Tech)
Specialization: Artificial Intelligence and Machine Learning
Duration: 2 Years (4 Semesters)
Academic Year: 2025-26

Program Structure¶

Semester-wise Breakdown¶

The M.Tech program is divided into 4 semesters over 2 years, with a focus on both theoretical foundations and practical applications of AI and ML.

Semester 1 (Current)¶

• Mathematical Foundations for Machine Learning (S1-25_AIMLCZC416)
• Deep Neural Networks (S1-25_AIMLCZG511)
• Introduction to Statistical Methods (S1-25_AIMLCZC418)
• Machine Learning (S1-25_AIMLCZG565)

Semester 2 (Upcoming)¶

Course details to be updated

Semester 3¶

Course details to be updated

Semester 4¶

• Thesis/Dissertation
• Research Project

Program Objectives¶

The M.Tech in AI & ML program aims to:

1. Build Strong Foundations: Develop deep understanding of mathematical, statistical, and computational principles underlying AI and ML
2. Practical Skills: Gain hands-on experience with modern ML frameworks and tools
3. Research Capability: Develop ability to conduct independent research in AI/ML
4. Problem Solving: Apply AI/ML techniques to solve real-world problems
5. Ethical Awareness: Understand ethical implications and responsible AI practices

Learning Outcomes¶

Upon completion of this program, graduates will be able to:

• ✅ Design and implement machine learning algorithms
• ✅ Build and train deep neural networks
• ✅ Apply statistical methods for data analysis
• ✅ Conduct research in AI/ML domains
• ✅ Deploy ML models in production environments
• ✅ Stay current with evolving AI/ML technologies

Career Opportunities¶

This program prepares students for roles such as:

• Machine Learning Engineer
• Data Scientist
• AI Research Scientist
• Deep Learning Engineer
• Computer Vision Engineer
• Natural Language Processing Engineer
• AI/ML Consultant
• Research and Development Engineer

Key Focus Areas¶

1. Machine Learning¶

• Supervised and unsupervised learning
• Reinforcement learning
• Ensemble methods
• Feature engineering

2. Deep Learning¶

• Neural network architectures
• CNNs, RNNs, Transformers
• Generative models
• Transfer learning

3. Mathematical Foundations¶

• Linear algebra
• Calculus and optimization
• Probability and statistics
• Information theory

4. Applications¶

• Computer vision
• Natural language processing
• Speech recognition
• Recommender systems
• Time series analysis

Research Areas¶

Students can explore research in:

• Advanced deep learning architectures
• Explainable AI (XAI)
• Federated learning
• AI for healthcare
• Autonomous systems
• AI ethics and fairness

Program Highlights¶

Assessment Methods¶

• Written Examinations
• Programming Assignments
• Course Projects
• Presentations
• Research Paper Reviews
• Thesis/Dissertation

For more detailed program information, please refer to the official program documentation.