OCS 351 Artificial Intelligence and Machine Learning Fundamentals question papers

Question papers

• November/December 2024

  Question paper code 41524

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Syllabus

1. Unit 1: Intelligent Agent and Uninformed Search6 hours

   Introduction · Foundations of AI · History of AI · The state of the art · Risks and Benefits of AI · Intelligent Agents · Nature of Environment · Structure of Agent · Problem Solving Agents · Formulating Problems · Uninformed Search · Breadth First Search · Dijkstra's algorithm or uniform-cost search · Depth First Search · Depth Limited Search

2. Unit 2: Problem Solving with Search Techniques6 hours

   Informed Search · Greedy Best First · A* algorithm · Adversarial Game and Search · Game theory · Optimal decisions in game · Min Max Search algorithm · Alpha-beta pruning · Constraint Satisfaction Problems (CSP) · Examples · Map Coloring · Job Scheduling · Backtracking Search for CSP

3. Unit 3: Learning6 hours

   Machine Learning: Definitions · Classification · Regression · approaches of machine learning models · Types of learning · Probability · Basics · Linear Algebra · Hypothesis space and inductive bias · Evaluation · Training and test sets · cross validation · Concept of over fitting · under fitting · Bias and Variance · Regression: Linear Regression · Logistic Regression

4. Unit 4: Supervised Learning6 hours

   Neural Network: Introduction · Perceptron Networks · Adaline · Back propagation networks · Decision Tree: Entropy · Information gain · Gini Impurity · classification algorithm · Rule based Classification · Naive Bayesian classification · Support Vector Machines (SVM)

5. Unit 5: Unsupervised Learning6 hours

   Unsupervised Learning · Principle Component Analysis · Neural Network: Fixed Weight Competitive Nets · Kohonen Self-Organizing Feature Maps · Clustering: Definition · Types of Clustering · Hierarchical clustering algorithms · k-means algorithm