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B-TECH in Artificial Intelligence And Machine Learning at Chaitanya Degree & PG College
Hanamkonda, Telangana
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About the Specialization
What is Artificial Intelligence and Machine Learning at Chaitanya Degree & PG College Hanamkonda?
This Artificial Intelligence and Machine Learning program at Chaitanya Institute of Technology and Sciences, Hanamkonda, focuses on equipping students with advanced knowledge and practical skills in cutting-edge AI and ML technologies. In the rapidly evolving Indian tech landscape, this specialization is highly relevant, addressing the surging demand for skilled professionals in areas like data science, intelligent systems, and automation. The program distinguishes itself through a robust curriculum covering foundational mathematics, programming, and advanced topics such as deep learning, reinforcement learning, and natural language processing, aligning with current industry trends.
Who Should Apply?
This program is ideal for aspiring engineers and innovators eager to delve into the transformative world of artificial intelligence and machine learning. It primarily targets fresh graduates seeking entry into high-growth tech roles within AI/ML domains across various Indian sectors. Additionally, working professionals with a computing background looking to upskill in specialized areas like data analytics, predictive modeling, or automation, will find the curriculum highly beneficial. Candidates typically possess strong analytical skills and a foundational understanding of mathematics and programming, seeking to transition into advanced technological careers.
Why Choose This Course?
Graduates of this program can expect diverse and rewarding career paths within India''''s thriving AI ecosystem. Roles such as AI Engineer, Machine Learning Scientist, Data Scientist, NLP Engineer, and Computer Vision Engineer are common. Entry-level salaries in India for these roles typically range from INR 4-8 LPA, with experienced professionals commanding significantly higher packages of INR 15-30+ LPA depending on expertise and company. The program also aligns with requirements for various industry certifications in AI/ML, fostering continuous professional growth and enabling graduates to contribute to India''''s digital transformation journey.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate significant effort to mastering programming logic and data structures using Python and C. Actively solve problems on coding platforms to build strong foundational problem-solving skills, crucial for all subsequent AI/ML courses.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, C language tutorials
Career Connection
Strong programming skills are non-negotiable for AI/ML roles, serving as the bedrock for implementing algorithms and developing intelligent systems in India''''s competitive tech industry.
Build a Solid Mathematical Base- (Semester 1-2)
Focus intently on Linear Algebra, Calculus, Probability, and Statistics. These mathematical concepts are the theoretical underpinnings of machine learning algorithms. Utilize online courses or textbooks to deepen understanding beyond classroom lectures.
Tools & Resources
Khan Academy, NPTEL courses for mathematics, Specific textbooks (e.g., Gilbert Strang for Linear Algebra)
Career Connection
A robust understanding of mathematics is essential for comprehending, debugging, and innovating on AI/ML models, setting graduates apart for research-oriented or advanced development roles.
Engage in Peer Learning and Study Groups- (Semester 1-2)
Form study groups with peers to discuss complex topics, solve problems collaboratively, and explain concepts to each other. This enhances understanding, identifies knowledge gaps, and develops teamwork skills.
Tools & Resources
College library study rooms, Online collaboration tools, Whiteboards
Career Connection
Teamwork and communication are vital in tech companies. Peer learning simulates collaborative work environments, fostering essential soft skills desired by Indian employers.
Intermediate Stage
Apply AI/ML Concepts through Mini-Projects- (Semester 3-5)
Proactively seek out or create small-scale AI/ML projects, even beyond coursework. Implement algorithms learned in Machine Learning and Deep Learning courses using real datasets. Participate in hackathons focused on AI/ML challenges.
Tools & Resources
Kaggle, GitHub, scikit-learn, TensorFlow, Keras, PyTorch, Public datasets
Career Connection
Practical project experience is highly valued by Indian recruiters. A strong project portfolio demonstrates applied skills and problem-solving abilities, directly impacting internship and placement opportunities.
Pursue Relevant Internships- (Semester 4-5 (especially during summer breaks))
Actively search for and complete internships (even short-term ones) at startups, IT firms, or research institutions working in AI/ML. This provides invaluable industry exposure and helps in understanding real-world challenges and workflows.
Tools & Resources
Internshala, LinkedIn, College placement cell, Company career pages
Career Connection
Internships often convert into full-time offers and significantly boost employability by providing practical experience and networking opportunities within the Indian tech industry.
Specialize through Electives and Online Certifications- (Semester 4-5)
Based on interest and career goals, choose professional electives wisely (e.g., NLP, Computer Vision). Supplement coursework with specialized online certifications in areas like Deep Learning Specialization (Coursera) or specific AI tools.
Tools & Resources
Coursera, edX, NPTEL, Udemy, Google AI certifications, AWS ML certifications
Career Connection
Specialization makes you a more attractive candidate for niche roles in companies seeking specific AI/ML expertise, enhancing career progression and earning potential in India.
Advanced Stage
Develop a Capstone Project with Impact- (Semester 7-8)
Undertake a significant final year project that addresses a real-world problem, ideally with potential for publication or industry application. Focus on innovation, thorough implementation, and clear documentation.
Tools & Resources
Advanced ML/DL frameworks, Cloud platforms (AWS, Azure, GCP), Project management tools, Academic journals
Career Connection
A strong capstone project is a key differentiator in placements, showcasing comprehensive skill application, research capability, and the ability to deliver substantial AI/ML solutions to Indian companies.
Ace Placement Preparation & Networking- (Semester 6-8)
Actively participate in campus placement training programs. Practice aptitude tests, technical interviews (especially AI/ML concepts), and mock group discussions. Network with alumni and industry professionals through LinkedIn and college events.
Tools & Resources
Placement cell resources, Glassdoor, LinkedIn, Mock interview platforms
Career Connection
Effective placement preparation maximizes chances of securing desirable job offers from top AI/ML companies in India. Networking can open doors to unadvertised opportunities.
Explore Research and Higher Education Options- (Semester 7-8)
If interested in research or academia, engage with faculty on research projects, attend technical conferences, and consider preparing for competitive exams like GATE or GRE for higher studies in India or abroad.
Tools & Resources
Research labs, Academic conferences, NPTEL courses, GATE/GRE preparation materials
Career Connection
Research experience or advanced degrees open doors to R&D roles, academic positions, or specialized roles requiring deeper theoretical understanding in India and globally.
Program Structure and Curriculum
Eligibility:
- Intermediate (10+2) with MPC or equivalent, qualified in TS EAMCET (As per general B.Tech admission norms in Telangana)
Duration: 8 semesters / 4 years
Credits: 163.5 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA101BS | Linear Algebra & Calculus | Core | 4 | Matrices and System of Linear Equations, Eigenvalues and Eigenvectors, Calculus of Single Variable, Functions of Several Variables, Sequences and Series |
| AP102BS | Applied Physics | Core | 3 | Wave Optics, Lasers and Fiber Optics, Quantum Mechanics, Semiconductor Physics, Dielectric and Magnetic Properties |
| CY103BS | Engineering Chemistry | Core | 3 | Water and its Treatment, Electrochemistry and Corrosion, Polymers, Energy Sources, Instrumental Methods |
| CS104ES | Programming for Problem Solving | Core | 3 | Introduction to Programming, Conditional Statements and Loops, Functions and Arrays, Pointers and Strings, Structures and File Handling |
| ME105ES | Engineering Graphics | Core | 3 | Orthographic Projections, Projections of Planes, Projections of Solids, Sections and Development of Surfaces, Isometric and Perspective Projections |
| AP106BS | Applied Physics Lab | Lab | 1.5 | Optical Instruments, Semiconductor Devices, Magnetic Fields, RC Circuits, Characteristics of Lasers |
| CY107BS | Engineering Chemistry Lab | Lab | 1.5 | Volumetric Analysis, Preparation of Polymers, Water Quality Tests, Conductometric Titrations, pH Metric Titrations |
| CS108ES | Programming for Problem Solving Lab | Lab | 1.5 | C Program Structure, Control Flow Statements, Functions and Arrays Implementation, Pointers and String Operations, Structures and File I/O |
| EN109HS | English Language and Communication Skills Lab | Lab | 2 | Listening Comprehension, Pronunciation Practice, Role Play and Debates, Group Discussions, Presentations and Public Speaking |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EN201HS | English for Skill Enhancement | Core | 2 | Reading Skills, Vocabulary Building, Grammar and Writing Skills, Soft Skills Development, Professional Communication |
| MA202BS | Ordinary Differential Equations and Vector Calculus | Core | 4 | First Order Differential Equations, Higher Order Differential Equations, Laplace Transforms, Vector Differentiation, Vector Integration |
| PH203BS | Engineering Physics | Core | 3 | Wave Mechanics, Crystal Structures, Dielectric Properties, Magnetic Properties, Superconductors |
| EE204ES | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines |
| CS205ES | Data Structures | Core | 3 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees, Graphs and Hashing |
| PH206BS | Engineering Physics Lab | Lab | 1.5 | Diffraction and Interference, Hall Effect, Energy Gap of Semiconductor, LCR Circuit, Characteristics of LED |
| EE207ES | Basic Electrical Engineering Lab | Lab | 1.5 | Verification of Circuit Laws, Measurement of Electrical Quantities, Testing of DC Machines, Testing of AC Machines, PN Junction Diode Characteristics |
| CS208ES | Data Structures Lab | Lab | 1.5 | Array and Linked List Operations, Stack and Queue Implementations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Techniques |
| ME209ES | Workshop/Manufacturing Practices | Lab | 2 | Carpentry and Fitting, Welding and Foundry, Blacksmithing, Sheet Metal Operations, Machine Shop Operations |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA301BS | Probability and Statistics with R Programming | Core | 3 | Probability Distributions, Sampling Distributions, Estimation and Hypothesis Testing, Correlation and Regression, R Programming Fundamentals |
| AI302PC | Discrete Mathematics | Core | 3 | Mathematical Logic, Set Theory and Relations, Functions and Combinatorics, Graph Theory, Algebraic Structures |
| AI303PC | Data Base Management Systems | Core | 3 | Introduction to DBMS, ER Model and Relational Model, SQL Queries, Normalization, Transaction Management and Concurrency Control |
| AI304PC | Object Oriented Programming with Python | Core | 3 | Python Basics, Object Oriented Paradigms, Classes and Objects, Inheritance and Polymorphism, Exception Handling and File I/O |
| AI305PC | Computer Organization & Architecture | Core | 3 | Basic Computer Organization, CPU Design, Memory Organization, Input/Output Organization, Pipelining and Parallel Processing |
| AI306PC | Data Base Management Systems Lab | Lab | 1.5 | SQL DDL and DML Commands, Constraints and Joins, Views and Sequences, Stored Procedures and Functions, Triggers and Cursors |
| AI307PC | Object Oriented Programming with Python Lab | Lab | 1.5 | Python Program Development, Class and Object Creation, Inheritance and Polymorphism Implementation, File Handling in Python, GUI Programming with Python |
| AI308PC | AI&ML Hardware & Software Lab | Lab | 1.5 | Linux Operating System Commands, Shell Scripting, Python Libraries for AI/ML, Data Preprocessing Techniques, Virtual Environments for AI/ML |
| MC309CI | Constitution of India | Mandatory Course | 0 | Constituent Assembly and Preamble, Fundamental Rights and Duties, Directive Principles of State Policy, Union and State Governments, Constitutional Amendments |
| MC310CI | Environmental Science | Mandatory Course | 0 | Ecosystems and Biodiversity, Environmental Pollution, Global Environmental Issues, Solid Waste Management, Environmental Protection Acts |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI401PC | Operating Systems | Core | 3 | Operating System Concepts, Process Management and CPU Scheduling, Memory Management, File Systems, I/O Systems and Deadlocks |
| AI402PC | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis Techniques, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms and NP-Completeness |
| AI403PC | Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms, Knowledge Representation and Reasoning, Machine Learning Basics |
| AI404PC | Foundations of Data Science | Core | 3 | Data Science Life Cycle, Data Collection and Cleaning, Exploratory Data Analysis, Data Visualization, Statistical Inference |
| AI405PE | Data Warehousing & Data Mining (Professional Elective - I) | Professional Elective | 3 | Data Warehousing Concepts, OLAP Operations, Data Preprocessing, Association Rule Mining, Classification and Clustering Techniques |
| AI406PC | Operating Systems Lab | Lab | 1.5 | Linux Commands and Shell Scripting, Process Management, CPU Scheduling Algorithms, Inter-process Communication, Memory Management Techniques |
| AI407PC | Artificial Intelligence Lab | Lab | 1.5 | Python for AI Programming, Uninformed and Informed Search Algorithms, Constraint Satisfaction Problems, Logic Programming (Prolog basics), AI Planning |
| AI408PC | Data Science with Python Lab | Lab | 1.5 | NumPy for Numerical Operations, Pandas for Data Manipulation, Matplotlib and Seaborn for Visualization, Data Cleaning and Transformation, Exploratory Data Analysis using Python |
| AI409HS | Advanced English Language & Communication Skills Lab | Lab | 2 | Advanced Presentation Skills, Interview Preparation Strategies, Group Discussion Techniques, Resume and Cover Letter Writing, Professional Etiquette |
| AI410SK | Data Analysis with R (Skill Oriented Course - I) | Skill Oriented Course | 2 | R Programming Basics, Data Import and Export in R, Data Manipulation with Dplyr, Statistical Graphics with Ggplot2, Basic Statistical Analysis in R |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI501PC | Machine Learning | Core | 3 | Introduction to Machine Learning, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Model Evaluation and Selection, Ensemble Methods and Dimensionality Reduction |
| AI502PC | Deep Learning | Core | 3 | Neural Network Fundamentals, Perceptron and Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Optimization Techniques and Regularization |
| AI503PE | Natural Language Processing (Professional Elective - II) | Professional Elective | 3 | NLP Fundamentals, Text Preprocessing and Tokenization, N-grams and Language Models, Part-of-Speech Tagging, Sentiment Analysis and Machine Translation |
| AI504PE | Recommender Systems (Professional Elective - III) | Professional Elective | 3 | Introduction to Recommender Systems, Collaborative Filtering, Content-Based Filtering, Hybrid Recommender Systems, Evaluation Metrics and Cold Start Problem |
| AI505HS | Universal Human Values | Core | 3 | Introduction to Value Education, Harmony in the Human Being, Harmony in Family and Society, Harmony in Nature/Existence, Professional Ethics |
| AI506PC | Machine Learning Lab | Lab | 1.5 | Scikit-learn for ML Algorithms, Classification Algorithms Implementation, Regression Algorithms Implementation, Clustering Techniques, Feature Engineering and Model Tuning |
| AI507PC | Deep Learning Lab | Lab | 1.5 | TensorFlow/Keras for Deep Learning, CNNs for Image Classification, RNNs for Sequence Data, Transfer Learning Techniques, Generative Models Implementation |
| AI508PW | Mini Project | Project | 2 | Problem Identification, Literature Survey, System Design, Implementation and Testing, Report Writing and Presentation |
| AI509SK | Data Visualization with Tableau (Skill Oriented Course - II) | Skill Oriented Course | 2 | Tableau Interface and Data Connection, Creating Basic Visualizations, Dashboards and Storytelling, Advanced Chart Types, Calculated Fields and Parameters |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI601PC | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning (Q-Learning, SARSA), Deep Reinforcement Learning |
| AI602PC | Computer Networks | Core | 3 | Network Topologies and Models, Data Link Layer, Network Layer, Transport Layer, Application Layer |
| AI603PE | Cloud Computing (Professional Elective - IV) | Professional Elective | 3 | Cloud Computing Concepts, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization Technology, Cloud Security and Management |
| AI604PE | Ethics in AI (Professional Elective - V) | Professional Elective | 3 | Introduction to AI Ethics, Bias and Fairness in AI, Accountability and Transparency, Privacy and Surveillance, Societal Impact of AI |
| AI605OE | Open Elective - I | Open Elective | 3 | |
| AI606PC | Reinforcement Learning Lab | Lab | 1.5 | OpenAI Gym Environments, Q-Learning Implementation, SARSA Algorithm, Policy Gradient Methods, Deep Q-Networks (DQN) |
| AI607PC | Computer Networks Lab | Lab | 1.5 | Network Configuration and Troubleshooting, Socket Programming, TCP/UDP Protocol Implementation, Routing Protocols, Network Security Tools |
| AI668PW | Internship (30 days) | Internship | 2 | Industry Exposure, Practical Skill Application, Professional Communication, Project Development, Technical Report Writing |
| AI609SK | Big Data Analytics Lab (Skill Oriented Course - III) | Skill Oriented Course | 2 | Hadoop Distributed File System (HDFS), MapReduce Programming, Apache Spark for Data Processing, Hive and Pig for Data Analysis, NoSQL Databases |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI701PE | Edge AI (Professional Elective - VI) | Professional Elective | 3 | Edge Computing Architecture, IoT Devices and AI, On-device Machine Learning, Model Optimization for Edge Devices, Edge AI Applications |
| AI702OE | Open Elective - II | Open Elective | 3 | |
| AI703PC | Technical Seminar | Seminar | 2 | Research Methodology, Literature Review, Technical Presentation Skills, Current Trends in AI/ML, Effective Communication |
| AI704PW | Project Work - I | Project | 6 | Problem Statement Definition, Detailed System Design, Initial Implementation and Module Testing, Intermediate Report Generation, Project Management |
| AI705SK | Robotic Process Automation Lab (Skill Oriented Course - IV) | Skill Oriented Course | 2 | Introduction to RPA Tools (e.g., UiPath), Task Automation, Bot Development Lifecycle, Workflow Design and Implementation, Exception Handling in RPA |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI801PE | Generative AI (Professional Elective - VII) | Professional Elective | 3 | Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, Large Language Models (LLMs), Applications of Generative AI |
| AI802PE | Conversational AI (Professional Elective - VIII) | Professional Elective | 3 | Chatbot Architectures, Natural Language Understanding (NLU), Natural Language Generation (NLG), Dialogue Management, Speech Recognition and Synthesis |
| AI803PW | Project Work - II | Project | 10 | Advanced System Implementation, Testing and Debugging, Performance Optimization, Final Report and Documentation, Project Defense (Viva-Voce) |
