Program

Integrated Master of Science in Artificial Intelligence and Machine Learning

Program Code

MSCAIML

Level

5 Years Integrated

Duration

5 Year

Department name

Artificial Intelligence and Machine Learning
Semester Sr no CourseCode Course CourseCredit
Sem-1 1 EC-101 Yoga 2
Sem-1 2 FC-101 Communication Skills 2
Sem-1 3 CC-101 Mathematical Basics 3
Sem-1 4 CC-102 Descriptive Statistics and Probability 3
Sem-1 5 CC-103 Introduction to Artificial Intelligence & 3
Sem-1 6 CC-103_1 Machine Learning 3
Sem-1 7 CC-104 Basics of Computer Organization & 3
Sem-1 8 CC-104_1 Architecture 3
Sem-1 9 CC-104_2 Information and Communication 3
Sem-1 10 CC_105_2 Technology (ICT) 3
Sem-1 11 CC-106 (P) Practical Based on CC-101& CC-102 3
Sem-1 12 CC-107 (P) Practical Based on CC-105 3
Sem-10 1 CC-511 Dissertation/ Project Work 16
Sem-10 2 CC-512 Seminar/ Symposium/ Conference 4
Sem-10 3 CC-513 Research Paper/ MOOCs/ Internship/ OJT 5
Sem-2 1 EC-111 Environmental Studies 2
Sem-2 2 FC-111 Commercial Communication 2
Sem-2 3 CC-111 Calculus and Introduction to Matrices 3
Sem-2 4 CC-112 Numerical and Statistical Methods 3
Sem-2 5 CC-113 Algorithms and Data Structures 3
Sem-2 6 CC-114 Object-Oriented Programming with JAVA 3
Sem-2 7 CC-115 Programming with PYTHON 3
Sem-2 8 CC-116 (P) Practical Based on CC-114 3
Sem-2 9 CC-117 (P) Practical Based on CC-115 3
Sem-3 1 FC-201 Soft Skills I 2
Sem-3 2 CC-201 Matrix Algebra and Calculus 3
Sem-3 3 CC-202 Random Variable and Distributions 3
Sem-3 4 CC-203 Discrete Mathematics 3
Sem-3 5 CC-204 Operating System Concepts 4
Sem-3 6 CC-205 Database Management Systems using SQL and PL/SQL 4
Sem-3 7 CC-206 (P) Data Analysis using Excel 3
Sem-3 8 CC-207 (P) Practical Based on CC-205 3
Sem-4 1 FC-211 Soft Skills II 2
Sem-4 2 CC-211 Linear Algebra 3
Sem-4 3 CC-212 Statistical Inference Theory 3
Sem-4 4 CC-213 Vector Calculus 3
Sem-4 5 CC-214 Introduction to Natural Language Processing 4
Sem-4 6 CC-215 Python for Machine Learning 4
Sem-4 7 CC-216 (P) R Programming – I 3
Sem-4 8 CC-217 (P) Practical Based on CC-215 3
Sem-5 1 FC-301 Scientific Writing 2
Sem-5 2 CC-301 Differential Equations 3
Sem-5 3 CC-302 Regression Theory 3
Sem-5 4 CC-303 Distributed Platforms 4
Sem-5 5 CC-304 Supervised Machine Learning 4
Sem-5 6 CC-305 (P) Data Visualization 3
Sem-5 7 CC-306 (P) Practical Based on CC-303 3
Sem-5 8 CC-307 (P) Practical Based on CC-304 3
Sem-6 1 FC-311 Personality Development 2
Sem-6 2 CC-311 Operations Research 4
Sem-6 3 CC-312 Research Methodology 3
Sem-6 4 CC-313 Unsupervised Machine Learning 4
Sem-6 5 CC-314 & CC-315 PROJECT – I: Mini-Project 6
Sem-6 6 CC-316 (P) R Programming – II 3
Sem-6 7 CC-317 (P) Practical Based on CC-313 3
Sem-7 1 CC-401 Advanced Algorithms 4
Sem-7 2 CC-402 Image Processing 4
Sem-7 3 CC-403 Deep Learning 4
Sem-7 4 CC-404 (P) Practical Based on CC-403 4
Sem-7 5 CC-405 (P) Power BI & Tableau 4
Sem-7 6 CC-406 PROJECT – II: Project 5
Sem-8 1 CC-411 Multivariate Analysis 4
Sem-8 2 CC-412 Cloud Computing 4
Sem-8 3 CC-413 Big Data Analytics 4
Sem-8 4 CC-414 Advanced NLP 4
Sem-8 5 CC-415 (P) Practical Based on CC-414 4
Sem-8 6 CC-416 PROJECT – III: Project 5
Sem-9 1 CC-501 Cyber Security 4
Sem-9 2 CC-502 Cloud Computing-II 4
Sem-9 3 CC-503 Blockchain Technology 4
Sem-9 4 CC-504 Reinforcement Learning 4
Sem-9 5 CC-505 (P) Practical Based on CC-504 4
Sem-9 6 CC-506 PROJECT – IV: Project 5
Intake
CourseType Shift
Sanctioned Intake
Fees
General EWS SEBC SC ST Male Female
HPP/Self Finance Noon 60 6 12 3 3 37600 37600
HPP/Self Finance Noon 45 3 6 0 3 37600 37600
Eligibility Criteria
  • PO1: 1. Foundational Knowledge: Demonstrate a strong understanding of the fundamental concepts in artificial intelligence, machine learning, data science, and related fields such as statistics and probability.
  • PO2: 10. Continuous Learning: Cultivate the ability to keep up with the rapidly evolving field of AI and ML, staying updated with the latest trends, tools, and technologies, and pursuing lifelong learning and professional development.
  • PO3: 2. Programming and Implementation Skills: Gain proficiency in programming languages commonly used in AI and ML, such as Python, R.
  • PO4: 3. Data Analysis and Preprocessing: Ability to collect, clean, and preprocess data, perform exploratory data analysis, and transform data into suitable formats for training AI/ML models.
  • PO5: 4. Model Development and Evaluation: Design, implement, and evaluate various machine learning models including supervised, unsupervised, and reinforcement learning models, while understanding their strengths and limitations.
  • PO6: 5. Algorithmic Understanding: Develop a deep understanding of algorithms and techniques used in AI/ML, including neural networks, decision trees, clustering, natural language processing, computer vision, and optimization techniques.
  • PO7: 6. Problem-Solving Skills: Apply AI and ML techniques to solve real-world problems across different domains such as healthcare, finance, robotics, and more, demonstrating critical thinking and innovative solutions.
  • PO8: 7. Ethics and Responsible AI: Understand the ethical implications of AI, including issues related to bias, privacy, and accountability, and develop AI solutions that are fair, transparent, and socially responsible.
  • PO9: 8. Communication and Collaboration: Effectively communicate technical concepts, findings, and results of AI/ML models to both technical and non-technical stakeholders and work collaboratively in interdisciplinary teams.
  • PO10: 9. Research and Innovation: Engage in research and contribute to the development of new AI/ML techniques, demonstrating the ability to conduct experiments, analyze results, and publish findings.
  • PSO1: 1. PSO 1: Mastery of AI/ML Techniques o Develop expertise in designing and implementing AI and ML algorithms, including neural networks, decision trees, clustering, natural language processing, and computer vision. Graduates will be capable of selecting and applying the most suitable techniques to address complex problems in various domains.
  • PSO2: 2. PSO 2: Advanced Data Handling and Analysis o Gain proficiency in handling large datasets, performing data preprocessing, and conducting exploratory data analysis. Graduates will be skilled in using data visualization, feature engineering, and data transformation techniques to prepare data for effective AI/ML model training.
  • PSO3: 3. PSO 3: Deployment of AI Models o Demonstrate the ability to deploy AI and ML models in real-world environments, including cloud-based and edge computing platforms. Graduates will be knowledgeable about software tools and frameworks such as TensorFlow, PyTorch, and Docker, and will understand the deployment pipeline, from development to production.
  • PSO4: 4. PSO 4: AI-driven Decision Making and Automation o Apply AI and ML to develop intelligent systems that automate decision-making processes, optimize operations, and improve efficiency across various industries such as healthcare, finance, manufacturing, and more. Graduates will be able to build solutions that enhance business outcomes through predictive analytics and data-driven insights.
  • PSO5: 5. PSO 5: Ethical and Responsible AI o Exhibit a strong understanding of the ethical implications of AI technologies, including issues related to bias, fairness, privacy, and transparency. Graduates will be committed to developing responsible AI solutions that adhere to ethical standards and regulatory requirements.
  • PSO6: 6. PSO 6: Research and Innovation in AI o Engage in innovative research in AI/ML, contributing to the advancement of the field. Graduates will be capable of conducting independent research, publishing their findings, and staying updated with the latest trends and technologies in AI/ML.
  • PSO7: 7. PSO 7: Communication and Leadership in AI Projects o Effectively communicate AI/ML concepts, project goals, and results to diverse stakeholders, including technical teams, business leaders, and non-technical audiences. Graduates will be prepared to lead AI projects, manage cross-functional teams, and make strategic decisions in AI implementations.
Subject Name: Communication Skills

Statements: 1. Proficiency in Verbal and Non-Verbal Communication: Students will enhance their ability to effectively communicate ideas and information through both verbal and non-verbal means, including active listening, clear articulation, body language, and tone of voice. 2. Writing Skills for Various Contexts: Students will develop strong writing skills tailored to different contexts, such as academic, professional, and informal settings, with a focus on structure, clarity, grammar, and audience appropriateness. 3. Critical Thinking and Interpersonal Communication: Students will improve their critical thinking abilities and interpersonal communication skills, enabling them to engage in meaningful dialogue, resolve conflicts, and collaborate effectively in team settings.

Subject Name: Introduction to Artificial Intelligence &

Statements: Introduction to Artificial Intelligence • Understand the history, evolution, and key concepts of AI. • Explain various AI techniques and their applications in solving real-world problems. • Develop simple AI models using basic algorithms and evaluate their performance.

Subject Name: Machine Learning

Statements: • Apply supervised, unsupervised, and reinforcement learning techniques to solve problems. • Implement algorithms such as regression, classification, clustering, and decision trees. • Evaluate model performance using appropriate metrics and techniques.

Subject Name: Basics of Computer Organization &

Statements: 1. Understanding of Computer Architecture: Students will gain a foundational understanding of computer architecture, including the basic components of a computer system such as the CPU, memory, and I/O devices, and how they interact to perform tasks. 2. Knowledge of Data Representation and Instruction Set Architecture (ISA): Students will learn how data is represented at the machine level, including binary, hexadecimal, and ASCII representations, and will understand the basics of Instruction Set Architecture (ISA), including how instructions are fetched, decoded, and executed by the CPU. 3. Comprehension of Assembly Language and Machine-Level Programming: Students will develop skills in writing and understanding simple assembly language programs, gaining insight into how high-level code is translated into machine-level instructions that the computer hardware can execute.

Subject Name: Commercial Communication

Statements: 1. Effective Communication Skills: Students will develop the ability to craft clear, concise, and persuasive communications, including emails, reports, proposals, and presentations, tailored to diverse professional audiences. 2. Mastery of Communication Strategies: Students will learn to apply various communication strategies and techniques for different commercial contexts, such as negotiations, client interactions, and team collaboration, with an emphasis on clarity, tone, and professionalism. 3. Understanding of Digital and Cross-Cultural Communication: Students will gain insights into the role of digital tools and platforms in commercial communications, and will be equipped to navigate and adapt their communication style for global and cross-cultural business environments.

Subject Name: Calculus and Introduction to Matrices

Statements: 1. Mastery of Fundamental Calculus Concepts: Students will develop a solid understanding of key calculus concepts, including limits, derivatives, integrals, and their applications in problem-solving, such as finding rates of change and areas under curves. 2. Ability to Solve Systems Using Matrices: Students will learn to perform basic operations with matrices, including addition, multiplication, and inversion, and will be able to apply these skills to solve systems of linear equations using techniques like Gaussian elimination and matrix inversion. 3. Application of Calculus and Matrices in Real-World Scenarios: Students will be able to apply calculus and matrix methods to model and solve practical problems in various fields, such as physics, engineering, economics, and data analysis, enhancing their analytical and problem-solving skills.

Subject Name: Algorithms and Data Structures

Statements: 1. Foundational Understanding: Students will develop a strong foundation in the principles of data structures and algorithms, including the ability to implement and utilize common data structures like arrays, linked lists, stacks, queues, trees, and graphs. 2. Algorithmic Problem-Solving: Students will be able to design, implement, and analyze algorithms for a variety of computational problems, applying techniques such as sorting, searching, recursion, and basic algorithmic paradigms like greedy and divide-and-conquer. 3. Complexity Analysis Skills: Students will learn to perform time and space complexity analysis on algorithms, enabling them to evaluate efficiency and optimize code performance for different data structures and algorithms.

Subject Name: Data Analysis using Excel

Statements: 1. Proficiency in Excel Functions and Tools: Students will gain proficiency in using essential Excel functions and tools for data analysis, including formulas, pivot tables, charts, and conditional formatting, to organize, manipulate, and visualize data effectively. 2. Data Cleaning and Preparation Skills: Students will learn techniques for cleaning and preparing datasets in Excel, such as handling missing values, removing duplicates, and performing data validation, to ensure data accuracy and reliability for analysis. 3. Application of Data Analysis Techniques: Students will develop the ability to apply various data analysis techniques in Excel, such as descriptive statistics, trend analysis, and data modeling, to draw meaningful insights and support decision-making in real-world scenarios.

Subject Name: Introduction to Natural Language Processing

Statements: • Understand key concepts in NLP, such as tokenization, stemming, and lemmatization. • Implement NLP tasks including sentiment analysis, text classification, and machine translation. • Apply language models and evaluate their performance using NLP-specific metrics.

Subject Name: Data Visualization

Statements: 1. Understanding of Data Visualization Principles: Students will learn the fundamental principles of data visualization, including the effective use of color, design, and chart types, to create clear and impactful visual representations of data. 2. Proficiency in Visualization Tools and Techniques: Students will develop skills in using data visualization tools (such as Tableau, Power BI, or Python libraries like Matplotlib and Seaborn) to design and implement a variety of visualizations, including bar charts, line graphs, scatter plots, and dashboards. 3. Ability to Communicate Insights Visually: Students will be able to translate complex data into easy-to-understand visual narratives, allowing them to effectively communicate insights and findings to diverse audiences, including stakeholders and decision-makers.

Subject Name: PROJECT – I: Mini-Project

Statements: • Integrate knowledge from various courses to design, develop, and deploy a comprehensive AI/ML solution to a real-world problem. • Demonstrate project management skills, including problem definition, solution design, implementation, and evaluation. • Present the project outcomes to a technical audience and defend the approach and decisions made during the project

Subject Name: Advanced Algorithms

Statements: 1. Design and Analysis Proficiency: Students will be able to design efficient algorithms for complex computational problems and rigorously analyze their time and space complexities using advanced techniques. 2. Algorithmic Paradigms Mastery: Students will gain a deep understanding of advanced algorithmic paradigms such as dynamic programming, greedy algorithms, and divide-and-conquer, and will be able to apply these paradigms to solve novel and complex problems. 3. Optimization and Approximation Skills: Students will develop the ability to solve optimization problems using advanced algorithms, including approximation and randomized algorithms, and will be able to evaluate the trade-offs between exact and heuristic approaches.

Subject Name: Deep Learning

Statements: • Understand the architecture and functioning of neural networks, including feedforward, convolutional, and recurrent networks. • Implement deep learning models using frameworks like TensorFlow or PyTorch. • Apply deep learning techniques to tasks such as image recognition, natural language processing, and time-series analysis.

Subject Name: PROJECT – II: Project

Statements: • Integrate knowledge from various courses to design, develop, and deploy a comprehensive AI/ML solution to a real-world problem. • Demonstrate project management skills, including problem definition, solution design, implementation, and evaluation. • Present the project outcomes to a technical audience and defend the approach and decisions made during the project

Subject Name: Cloud Computing

Statements: • Demonstrate the ability to use cloud-based AI/ML services (e.g., AWS SageMaker, Google AI Platform, Azure Machine Learning) for building, training, and deploying machine learning models efficiently. • Design and implement scalable AI/ML solutions in the cloud, leveraging cloud-native tools and technologies such as serverless computing, auto-scaling, and distributed computing to handle large-scale data and complex model requirements. • Analyze and apply best practices for managing data and ensuring security in cloud-based AI/ML projects, including data storage, processing, privacy, compliance, and access control within cloud environments.

Subject Name: Big Data Analytics

Statements: • Understand the challenges and techniques for handling large-scale data in AI systems. • Use big data tools like Hadoop, Spark, and NoSQL databases for data management and processing. • Design scalable AI solutions that can handle high volumes of data efficiently.

Subject Name: Advanced NLP

Statements: • Implement NLP tasks including sentiment analysis, text classification, and machine translation. • Apply language models and evaluate their performance using NLP-specific metrics.

Subject Name: PROJECT – III: Project

Statements: • Integrate knowledge from various courses to design, develop, and deploy a comprehensive AI/ML solution to a real-world problem. • Demonstrate project management skills, including problem definition, solution design, implementation, and evaluation. • Present the project outcomes to a technical audience and defend the approach and decisions made during the project

Subject Name: Cyber Security

Statements: • Identify and analyze various cyber threats (e.g., malware, phishing, ransomware) and implement appropriate defense mechanisms, such as firewalls, intrusion detection systems, and antivirus software. • Develop and apply security policies, procedures, and best practices to protect information systems, ensuring confidentiality, integrity, and data availability. • Demonstrate proficiency in network security techniques, including secure network design, encryption, cryptographic algorithms, and secure communication protocols. • Formulate and execute incident response plans to detect, respond to, and recover from cyber security incidents, including conducting forensic analysis and reporting. • Conduct ethical hacking and penetration testing to identify vulnerabilities in systems and applications, using industry-standard tools and techniques. • Evaluate legal, regulatory, and compliance requirements related to cyber security, including data protection laws, ethical considerations, and international standards.

Subject Name: Cloud Computing-II

Statements: • Demonstrate the ability to use cloud-based AI/ML services (e.g., AWS SageMaker, Google AI Platform, Azure Machine Learning) for building, training, and deploying machine learning models efficiently. • Design and implement scalable AI/ML solutions in the cloud, leveraging cloud-native tools and technologies such as serverless computing, auto-scaling, and distributed computing to handle large-scale data and complex model requirements. • Analyze and apply best practices for managing data and ensuring security in cloud-based AI/ML projects, including data storage, processing, privacy, compliance, and access control within cloud environments.

Subject Name: Reinforcement Learning

Statements: • Explain the concepts of agents, states, actions, rewards, and policies in reinforcement learning. • Implement reinforcement learning algorithms such as Q-learning and Deep Q-Networks (DQNs). • Apply reinforcement learning to solve complex decision-making problems.

Subject Name: PROJECT – IV: Project

Statements: • Integrate knowledge from various courses to design, develop, and deploy a comprehensive AI/ML solution to a real-world problem. • Demonstrate project management skills, including problem definition, solution design, implementation, and evaluation. • Present the project outcomes to a technical audience and defend the approach and decisions made during the project.

Academy Year Title Download
2021-2022 M.Sc. (Integrated) Artificial Intelligence & Machine Learning
2023-2024 Artificial Intelligence & Machine Learning Semester 2

Gujarat University

Online Admission
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