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M-TECH in Artificial Intelligence Machine Learning at Koneru Lakshmaiah Education Foundation (Deemed to be University)
Guntur, Andhra Pradesh
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About the Specialization
What is Artificial Intelligence & Machine Learning at Koneru Lakshmaiah Education Foundation (Deemed to be University) Guntur?
This M.Tech Artificial Intelligence & Machine Learning program at Koneru Lakshmaiah Education Foundation focuses on equipping students with advanced theoretical and practical skills in AI and ML. It is designed to meet the rapidly growing demand for skilled professionals in the Indian IT and research sectors, distinguishing itself through a strong emphasis on real-world applications and cutting-edge research.
Who Should Apply?
This program is ideal for fresh engineering graduates in CSE/IT or related fields, and working professionals from the software industry looking to upskill. It also suits career changers aspiring to transition into the dynamic AI/ML domain. Candidates should possess a strong foundational understanding of mathematics, programming, and an eagerness to solve complex problems.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative careers as AI/ML Engineers, Data Scientists, Research Scientists, or AI Consultants in India. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly more. The program prepares students for roles in product development, R&D, and innovation hubs across various Indian companies and MNCs.
Student Success Practices
Foundation Stage
Master Programming & Mathematical Foundations- (Semester 1)
Focus intensely on ''''Applied Mathematics for AI & ML'''' and ''''Advanced Data Structures & Algorithms''''. Utilize online platforms like NPTEL and edX for supplementary learning, and regularly practice coding problems on HackerRank or LeetCode to build problem-solving muscle. Engage with peers for collaborative learning sessions.
Tools & Resources
NPTEL, edX, HackerRank, LeetCode, MIT OpenCourseware (Mathematics)
Career Connection
A strong base in mathematics and algorithms is non-negotiable for AI/ML roles and crucial for clearing technical rounds in placements.
Develop Robust Research Acumen- (Semester 1)
Actively participate in discussions for ''''Research Methodology & IPR''''. Start reading research papers related to AI/ML, even if they seem advanced. Learn to critically evaluate methodologies and identify research gaps. Attend university research talks and guest lectures to broaden your perspective.
Tools & Resources
Google Scholar, ArXiv, Semantic Scholar, IEEE Xplore, ACM Digital Library
Career Connection
Essential for pursuing higher studies, R&D roles, and contributes to the quality of your M.Tech project and potential publications.
Initiate an AI/ML Project Portfolio Early- (Semester 1)
Begin working on small, independent AI/ML projects from the first semester. Apply concepts learned in ''''Machine Learning'''' to real datasets from platforms like UCI Machine Learning Repository or Kaggle. Document your code and findings meticulously on GitHub, even for simple implementations.
Tools & Resources
Kaggle, UCI ML Repository, GitHub, Python with scikit-learn, Jupyter Notebooks
Career Connection
Early projects demonstrate proactive learning and practical application, making your profile stand out during internship and placement drives.
Intermediate Stage
Dive Deep into Neural Networks and NLP- (Semester 2)
Excel in ''''Deep Learning'''' and ''''Natural Language Processing'''' courses by implementing various neural network architectures (CNNs, RNNs, Transformers) and NLP techniques (sentiment analysis, language generation). Experiment with frameworks like TensorFlow and PyTorch for building complex models.
Tools & Resources
TensorFlow, PyTorch, Keras, Hugging Face Transformers, Google Colab
Career Connection
Deep expertise in these areas opens doors to specialized roles in computer vision, natural language understanding, and generative AI.
Seek a Relevant Industry Internship- (Semester 2)
Leverage the university''''s placement cell and personal networks to secure an internship in an AI/ML focused role. Apply theoretical knowledge to solve real-world industry problems, gaining hands-on experience. Focus on learning industry best practices, project management, and professional communication skills during your internship.
Tools & Resources
University Placement Cell, LinkedIn, Internshala, Company career pages
Career Connection
Internships are crucial for industry exposure, networking, and often lead to pre-placement offers (PPOs), directly impacting your career launch.
Explore Specialization through Electives- (Semester 2)
Carefully choose electives based on your career interests. Dedicate extra time to these specialized areas, perhaps by pursuing advanced online certifications or building independent projects that demonstrate mastery in your chosen niche.
Tools & Resources
Online courses (Coursera, Udacity), Specialized books, Research papers, Project-based learning
Career Connection
Deepening your knowledge in a specific sub-field can lead to expert roles and differentiates you from generalists, aligning with specific industry demands.
Advanced Stage
Execute an Industry-Relevant M.Tech Project- (Semester 3-4)
Select a challenging M.Tech project that addresses a current industry problem or contributes to academic research. Collaborate with faculty, industry mentors, or even companies. Focus on delivering a deployable solution or a publishable research paper, documenting all phases thoroughly.
Tools & Resources
Git, Project management tools, Cloud platforms (AWS, Azure, GCP), Academic journals
Career Connection
A well-executed, impactful project is a strong selling point for placements, showcasing your ability to conduct independent research and deliver results.
Master Advanced Python and Deployment Skills- (Semester 3)
Utilize ''''Advanced Python Programming'''' to refine your coding skills for large-scale AI systems. Learn about MLOps, containerization (Docker), and cloud deployment strategies for AI models. Participate in hackathons to apply these skills in a time-bound, competitive environment.
Tools & Resources
Docker, Kubernetes, Flask/Django, FastAPI, AWS SageMaker, Azure ML, GCP AI Platform
Career Connection
MLOps and deployment skills are highly sought after, enabling you to transition from model development to bringing AI solutions to production.
Prepare Rigorously for Placements & Interviews- (Semester 3-4)
Begin comprehensive preparation for placements well in advance. Practice technical questions on data structures, algorithms, and core AI/ML concepts. Work on soft skills, communication, and mock interviews. Tailor your resume and cover letters to specific job descriptions.
Tools & Resources
LeetCode, GeeksforGeeks, InterviewBit, LinkedIn Learning (for soft skills), University career services
Career Connection
Effective preparation maximizes your chances of securing placements in top-tier companies and kickstarting a successful career immediately after graduation.
Program Structure and Curriculum
Eligibility:
- Candidate must have B.Tech./B.E./AMIE in relevant Engineering Discipline (CSE/IT/ECE/EEE/Software Engineering) or MCA or M.Sc.(Computers/IT/Applied Maths with Computer specialization) or equivalent with a minimum of 60% marks or CGPA 6.0/10.0 from UGC/AICTE recognized institutions/universities.
Duration: 4 semesters / 2 years
Credits: 74 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MT2001 | Applied Mathematics for AI & ML | Core | 4 | Linear Algebra for ML, Probability and Statistics, Optimization Techniques, Calculus for Machine Learning, Random Processes |
| 23MT2002 | Advanced Data Structures & Algorithms | Core | 4 | Analysis of Algorithms, Advanced Data Structures (Heaps, Trees), Graph Algorithms, Dynamic Programming, Complexity Classes (P, NP) |
| 23MT2003 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation and Validation, Ensemble Methods, Feature Engineering |
| 23MT2004 | Advanced Data Structures & Algorithms Lab | Lab | 2 | Implementation of ADTs, Graph Traversal Algorithms, Dynamic Programming Problems, Hashing Techniques, Sorting and Searching Algorithms |
| 23MT2005 | Machine Learning Lab | Lab | 2 | Python Libraries for ML, Implementing Supervised Models, Implementing Unsupervised Models, Data Preprocessing and Visualization, Hyperparameter Tuning |
| 23MT2006 | Research Methodology & IPR | Core | 4 | Research Problem Formulation, Research Design and Methods, Data Collection and Analysis, Report Writing, Intellectual Property Rights |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MT2007 | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders and GANs, Transfer Learning |
| 23MT2008 | Natural Language Processing | Core | 4 | Text Preprocessing, Language Models, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation |
| 23MT2009 | Deep Learning Lab | Lab | 2 | Building CNNs with Keras/PyTorch, Implementing RNNs for Sequence Data, Image Classification, Generative Model Implementation, Hyperparameter Optimization |
| 23MT21XX | Program Elective – I | Elective | 3 | Image Processing Fundamentals, Feature Extraction, Object Detection, Image Segmentation, Facial Recognition |
| 23MT21XX | Program Elective – II | Elective | 3 | Markov Decision Processes, Q-Learning and SARSA, Policy Gradient Methods, Deep Reinforcement Learning, Multi-Agent Systems |
| 23MT2010 | Seminar | Core | 2 | Literature Survey, Technical Presentation Skills, Report Writing, Critical Analysis, Public Speaking |
| 23MT2011 | Internship | Core | 2 | Industry Exposure, Practical Skill Application, Professional Networking, Problem Solving, Project Report |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MT2012 | Advanced Python Programming | Core | 4 | Advanced Data Structures in Python, Decorators, Generators, Metaclasses, Asynchronous Programming, Concurrency and Parallelism, Web Frameworks (Flask/Django) |
| 23MT22XX | Program Elective – III | Elective | 3 | Medical Imaging Analysis, AI in Drug Discovery, Clinical Decision Support Systems, Electronic Health Records, Personalized Medicine |
| 23MT22XX | Program Elective – IV | Elective | 3 | Hadoop Ecosystem, Apache Spark for Big Data, NoSQL Databases, Data Warehousing Concepts, Data Visualization Techniques |
| 23MT2013 | Advanced Python Programming Lab | Lab | 2 | File I/O and Data Persistence, Database Connectivity, Web Scraping and APIs, GUI Development, Building Microservices |
| 23MT2014 | Project Work - I | Project | 4 | Problem Identification and Scoping, Extensive Literature Review, System Design and Architecture, Methodology Selection, Initial Prototype Development |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MT2015 | Project Work - II | Project | 18 | Full System Implementation, Testing and Validation, Performance Analysis and Optimization, Thesis Writing and Documentation, Oral Presentation and Defense |
