This course introduces the foundations of AI and DL covering neural networks, ANN structures, and backpropagation. It explores sequential models including feedforward, recurrent, Seq2Seq, and LSTMs, followed by key generative models such as encoder-decoder architectures, GANs, and VAEs. Students also examine applications of Generative AI in art, text, music, healthcare, and finance, while discussing real-world challenges in deploying these models.
Prerequisites: Machine Learning, Deep Learning basics
This specialized course focuses on applying Artificial Intelligence techniques to financial data analysis and prediction. This course covers the fundamentals of financial modelling, including financial statements, Excel techniques, ratios, and KPIs. It introduces AI and Machine Learning concepts with Python, focusing on algorithms for financial datasets. Key applications include forecasting, credit risk assessment, fraud detection, and portfolio optimization. Students also gain exposure to tools like Power BI, Tableau, and AutoML, along with insights into FinTech, robo-advisors, and ethical considerations in AI.
Prerequisites: Probability and Statistics, Machine Learning basics
This course introduces the mathematical and logical foundations of computer science. Key themes include finite automata, regular expressions, context-free grammars, pushdown automata, and Turing machines. Students learn about computability, decidability, and complexity theory.
Prerequisites: Discrete Mathematics
This course introduces the principles of object-oriented programming using C++ and Java. Major themes include classes and objects, inheritance, polymorphism, abstraction, interfaces, exception handling, and file I/O. Students develop practical coding skills through laboratory assignments and mini-projects.
Prerequisites: Basic programming knowledge in C
This foundational course covers the fundamentals of structured programming in C, including variables, data types, arrays, pointers, functions, and file handling. Emphasis is placed on problem-solving and algorithmic thinking.
Prerequisites: None. Basic Logic.
This course focuses on database concepts, relational , SQL, normalization, indexing, transactions, and concurrency control. Students gain hands-on experience with database design and implementation using SQL-based systems.
Prerequisites: Data Structures and Algorithms
This course covers client-side and server-side web development. Topics include HTML, CSS, JavaScript, PHP, and frameworks for building dynamic web applications. Students also explore emerging technologies such as cloud-based deployments and responsive design.
Prerequisites: C Programming
Description: This course focuses on database concepts, relational algebra, SQL, normalization, indexing, transactions, and concurrency control. Students gain hands-on experience with database design and implementation using SQL-based systems.
Prerequisites: Data Structures and Algorithms