Category solved model papers

Acting Humanly: The Turing Test approach

The Turing Test, proposed by Alan Turing in 1950, is designed to provide an operational definition of intelligence by checking whether a machine’s behavior is indistinguishable from that of a…

Model-Free Methods

In reinforcement learning, model-free methods are used when: Examples: Monte Carlo (MC) Methods Temporal Difference (TD) Learning

REINFORCEMENT LEARNING AS MACHINE LEARNING

Differences between Reinforcement Learning & Supervised Learning Reinforcement Learning Supervised Learning No supervisor; no labelled dataset initially Has a supervisor; labelled dataset available Data points are dependent; each move/action affects…

Mean-Shift Clustering Algorithm

Mean-shift is a: Widely used in: How it works: Step-by-step (Algorithm ): Step 1: Choose a window (kernel) with a given bandwidth.Step 2: Place the window on a data point.Step…

Density-Based Methods (DBSCAN) and Grid-Based Approach (CLIQUE)

DENSITY-BASED METHODS: DBSCAN DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Key ideas: Density connectivity concepts: DBSCAN Algorithm Advantages: Complexity: depends on number of data points and ε-neighborhood computation. GRID-BASED…

PARTITIONAL CLUSTERING ALGORITHM (k-means)

k-means Algorithm 1️⃣ Choose number of clusters, k.2️⃣ Randomly choose k initial data points as centroids.3️⃣ Assign each data point to the nearest centroid (using Euclidean distance or similar).4️⃣ Compute…

BDS602 – AIML Important Questions with answers

BDS602 – Artificial Intelligence and Machine Learning Solved Important Questions with answers Module – 1 1] What is Artificial Intelligence? Discuss the 4 categories of AI, foundations and history of…