{"id":57631,"date":"2023-09-02T16:42:38","date_gmt":"2023-09-02T16:42:38","guid":{"rendered":"https:\/\/www.inspirenignite.com\/anna-university\/ocs351-artificial-intelligence-and-machine-learning-fundamentals-syllabus-for-mae-2021-regulation-open-elective-i\/"},"modified":"2023-09-02T16:42:38","modified_gmt":"2023-09-02T16:42:38","slug":"ocs351-artificial-intelligence-and-machine-learning-fundamentals-syllabus-for-mae-2021-regulation-open-elective-i","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/anna-university\/ocs351-artificial-intelligence-and-machine-learning-fundamentals-syllabus-for-mae-2021-regulation-open-elective-i\/","title":{"rendered":"OCS351: Artificial Intelligence and Machine Learning Fundamentals syllabus for MAE 2021 regulation (Open Elective-I)"},"content":{"rendered":"

Artificial Intelligence and Machine Learning Fundamentals detailed syllabus for Mechanical & Automation Engineering (MAE) for 2021 regulation curriculum has been taken from the Anna Universities<\/a> official website and presented for the MAE students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below. <\/p>\n

For Mechanical & Automation Engineering 6th Sem scheme and its subjects, do visit MAE 6th Sem 2021 regulation scheme<\/a>. For Open Elective-I scheme and its subjects refer to MAE Open Elective-I syllabus scheme<\/a>. The detailed syllabus of artificial intelligence and machine learning fundamentals is as follows. <\/p>\n

Course Objectives:<\/h4>\n

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Unit I<\/h4>\n

INTELLIGENT AGENT AND UNINFORMED SEARCH
\nIntroduction – 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 uniformcost search – Depth First Search – Depth Limited Search\n<\/p>\n

Unit II<\/h4>\n

PROBLEM SOLVING WITH SEARCH TECHNIQUES
\nInformed 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\n<\/p>\n

Unit III<\/h4>\n

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Unit IV<\/h4>\n

SUPERVISED LEARNING
\nNeural 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)\n<\/p>\n

Unit V<\/h4>\n

UNSUPERVISED LEARNING
\nUnsupervised 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\n<\/p>\n

Practical Exercises<\/h4>\n

Programs for Problem solving with Search<\/p>\n

    \n
  1. Implement breadth first search<\/li>\n
  2. Implement depth first search<\/li>\n
  3. Analysis of breadth first and depth first search in terms of time and space<\/li>\n
  4. Implement and compare Greedy and A* algorithms.<\/li>\n<\/ol>\n

    Supervised learning<\/i><\/p>\n

      \n
    1. Implement the non-parametric locally weighted regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs<\/li>\n
    2. Write a program to demonstrate the working of the decision tree based algorithm.<\/li>\n
    3. Build an artificial neural network by implementing the back propagation algorithm and test the same using appropriate data sets.<\/li>\n
    4. Write a program to implement the naive Bayesian classifier.<\/li>\n<\/ol>\n

      Unsupervised learning<\/i><\/p>\n

        \n
      1. Implementing neural network using self-organizing maps<\/li>\n
      2. Implementing k-Means algorithm to cluster a set of data.<\/li>\n
      3. Implementing hierarchical clustering algorithm.<\/li>\n<\/ol>\n

        Note:<\/i><\/p>\n