1.   Artificial Intelligence Systems

• Search Algorithms and Problem Solving: Uninformed and Informed Search (BFS, DFS, A*), Adversarial Search (Minimax, Alpha-Beta Pruning)

• Machine Learning Fundamentals: Supervised Learning (Linear/Logistic Regression, Decision Trees, SVM, k-NN), Unsupervised Learning (k-Means, PCA)

• Model Evaluation and Validation, Bias-Variance Tradeoff, Evaluation Metrics

• Neural Networks and Deep Learning: Perceptrons, MLPs, Activation Functions, Backpropagation, Gradient Descent Variants

• Modern Deep Learning Architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs, LSTMs), Attention Mechanisms and Transformers

• Training and Regularization Techniques: Overfitting prevention, Dropout, Batch Normalization, Loss Functions, Optimization Algorithms (Adam, RMSProp)

2.  Computer Networks

• Packet and circuit switching, delay and loss, Protocol layers

• Application layer: HTTP, FTP, SMTP, MIME, IMAP, DNS

• Transport layer: Multiplexing and demultiplexing, UDP, principles of reliable data transfer, TCP flow and congestion control

• Network layer: Router, IP addresses and protocols, Routing algorithms, Broadcast and multicast, Multiple access protocols

• Link layer: Error detection and correction, Link layer addressing, Ethernet, Switches 

3.  Database Management Systems 

• File structures (sequential/pile files, indexed files), Index structures (ISAM, B+tree, hashing)

• Relational Data Model: ER diagrams, Conceptual design, Relational Algebra

• Schema refinement, functional dependencies, normalization

• SQL DDL and DML operations, Query evaluation and optimization

• Transaction management, ACID properties, and concurrency control 

4.  Data Structures

• Memory requirement and running time analysis

• Sorting algorithms and their analysis, Searching methods

• Single and doubly linked list operations, Stacks and Queues

• Trees (Binary trees, AVL trees, Heaps) and Graph data structures 

5.  Digital Design

• Min-term and max-term forms, Logic Gates, NAND/NOR implementations

• Combinational logic: Half / full adder, half / full subtractor, multiplier, comparator

• Multiplexer, demultiplexer, decoder, encoder

• Sequential logic: Flip-flops, synchronous / asynchronous counters, timers, shift registers, RAM

6.  Discrete Mathematics

• Logic and Proofs, Sets, Functions, Relations

• Modular Arithmetic, Induction and Recursion

• Combinatorial Analysis, Counting principles, Pigeonhole principle

• Graphs and Trees fundamentals

7.  Operating Systems

• Processes, System calls, Threads, Inter-process / thread communications

• Synchronization, Semaphores, Deadlocks and handling techniques

• CPU Task scheduling algorithms

• Memory management, Paging, Segmentation, and Virtual memory

• File system management and implementation

8.  Programming Languages

• Language evaluation criteria, paradigms, and implementation methods

• Describing syntax and semantics, Lexical and syntax analysis

• Names, bindings, type checking, and scope rules

• Data types, Expressions, assignment statements, and control structures

• Subprograms, Parameter passing, Abstract data types, and encapsulation

9.  Software Engineering

• Software Process Models, Agile and Traditional Methodologies

• Requirements Engineering, Scenario-Based and Object-Oriented Modeling

• Software Architecture and Design Concepts, Design Patterns

• User Interface Analysis and Design Principles

• Software Quality Assurance, Verification and Validation, Software Testing Fundamentals

10.  Theory of Computation

• Deterministic and Non-Deterministic Finite State Automata, Regular Expressions, Pumping Lemma for Regular Languages

• Context-Free Languages (CFL), Push-Down Automata, Chomsky Normal Form, Pumping Lemma for CFL

• Turing Machines, Decidable and Enumerable Languages

• Complexity classes P & NP, NP-Complete Languages