🎓 Computer Science & Engineering Portal

Master Engineering Disciplines with Structured Notes

Comprehensive academic lecture notes, exam-oriented unit summaries, laboratory manuals, and previous year question papers designed strictly for university students.

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University Syllabi

AKTU & AICTE aligned semester credit guidelines.

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Exam Question Papers

Previous 5 years solved university semester papers.

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Lab Manuals & Viva

Practical codes with outputs and interview questions.

Core Subjects & Units Hub

Click on any specific unit to immediately view its lecture notes below

🎨 Computer Graphics (CG)

Scan Conversion, Bresenham Line & Circle, 2D/3D Transformations, Viewing & Clipping.

Unit 1: Raster Scan, DDA & Bresenham →
Unit 2: 2D & 3D Transformations →
Unit 3: Sutherland-Hodgman & Clipping →
Unit 4: Hidden Surface Elimination →

🧠 Machine Learning (ML / MLT)

Supervised/Unsupervised Learning, Regression, Decision Trees, SVM, Neural Nets & Clustering.

Unit 1: Linear & Logistic Regression →
Unit 2: Decision Trees & Support Vector (SVM) →
Unit 3: K-Means & Dimensionality Reduction →
Unit 4: Neural Networks & Gradient Descent →

🤖 Artificial Intelligence (AI)

Search Algorithms, First Order Logic, Probabilistic Reasoning, Expert Systems & Robotics.

Unit 1: Propositional Logic & Connectives →
Unit 2: Probabilistic Reasoning & Uncertainty →
Unit 3: State Space Search & Heuristics →
Unit 4: First Order Predicate Logic (FOL) →

🗄️ Database Management (DBMS)

ER-Modeling, Relational Algebra, SQL Queries, Normalization (1NF-BCNF) and ACID Transactions.

Unit 1: ER Model, Entities & Attributes →
Unit 2: Functional Dependencies & 1NF to BCNF →
Unit 3: ACID Properties & Concurrency Control →
Unit 4: Relational Algebra & Complex SQL Joins →

🌲 Data Structures & Algorithms

Arrays, Linked Lists, Stacks, Queues, Binary Trees, Graphs, Sorting & Asymptotic Analysis.

Unit 1: Arrays, Matrices & Recursion →
Unit 2: Stacks, Queues & Infix-to-Postfix →
Unit 3: Binary Trees, BST & AVL Rotations →
Unit 4: Graphs (BFS, DFS, Dijkstra, MST) →

⚡ Operating Systems

Process Scheduling, Deadlocks, Synchronization, Virtual Memory, Paging and Disk Management.

Unit 1: Process States, PCB & Multi-Threading →
Unit 2: CPU Scheduling (FCFS, SJF, RR) →
Unit 3: Deadlocks, Semaphores & Banker's Algo →
Unit 4: Virtual Memory, Paging & Disk Scheduling →

🌐 Computer Networks

OSI & TCP/IP Models, Error Detection, IPv4 Subnetting, Routing Protocols and TCP Handshake.

Unit 1: OSI vs TCP/IP Protocol Architectures →
Unit 2: Data Link Layer, Framing & Sliding Window →
Unit 3: IPv4 Addressing, Subnetting & Routing →
Unit 4: Transport Layer (TCP 3-Way Handshake) →

⚙️ Design of Algorithms (DAA)

Asymptotic Notations, Divide & Conquer, Dynamic Programming, Greedy Approach & Backtracking.

Unit 1: Time Complexity, Master's Theorem →
Unit 2: 0/1 Knapsack & Dynamic Programming →
Unit 3: Greedy Methods & Graph Algorithms →
Viewing All Lectures

Restriction Biases and Preference Biases

Difference between the types of inductive bias exhibited by ID3 and by the CANDIDATE- ELIMINATION Algorithm.

ID3:

  • ID3 searches a complete hypothesis space
  • It searches incompletely through this space, from simple to complex hypotheses, until its termination condition is met
  • Its inductive bias is solely a consequence of the ordering of hypotheses by its search strategy. Its hypothesis space introduces no additional bias

CANDIDATE-ELIMINATION Algorithm:

  • The version space CANDIDATE-ELIMINATION Algorithm searches an incomplete hypothesis space
  • It searches this space completely, finding every hypothesis consistent with the training data.
  • Its inductive bias is solely a consequence of the expressive power of its hypothesis representation. Its search strategy introduces no additional bias


Preference bias – The inductive bias of ID3 is a preference for certain hypotheses over others (e.g., preference for shorter hypotheses over larger hypotheses), with no hard restriction on the hypotheses that can be eventually enumerated. This form of bias is called a preference bias or a search bias.

Restriction bias – The bias of the CANDIDATE ELIMINATION algorithm is in the form of a categorical restriction on the set of hypotheses considered. This form of bias is typically called a restriction bias or a language bias.

Which type of inductive bias is preferred in order to generalize beyond the training data, a preference bias or restriction bias?

  • A preference bias is more desirable than a restriction bias, because it allows the learner to work within a complete hypothesis space that is assured to contain the unknown target function.
  • In contrast, a restriction bias that strictly limits the set of potential hypotheses is generally less desirable, because it introduces the possibility of excluding the unknown target function altogether.

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