OCS351: Artificial Intelligence and Machine Learning Fundamentals syllabus for Pharma 2021 regulation (Open Elective-I)

Artificial Intelligence and Machine Learning Fundamentals detailed syllabus for Pharmacy (Pharma) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the Pharma 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.

For Pharmacy 6th Sem scheme and its subjects, do visit Pharma 6th Sem 2021 regulation scheme. For Open Elective-I scheme and its subjects refer to Pharma Open Elective-I syllabus scheme. The detailed syllabus of artificial intelligence and machine learning fundamentals is as follows.

Course Objectives:

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Unit I

INTELLIGENT AGENT AND UNINFORMED SEARCH
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 uniformcost search – Depth First Search – Depth Limited Search

Unit II

PROBLEM SOLVING WITH SEARCH TECHNIQUES
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

Unit III

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Unit IV

SUPERVISED LEARNING
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)

Unit V

UNSUPERVISED LEARNING
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

Practical Exercises

Programs for Problem solving with Search

1. Implement breadth first search
2. Implement depth first search
3. Analysis of breadth first and depth first search in terms of time and space
4. Implement and compare Greedy and A* algorithms.

Supervised learning

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
2. Write a program to demonstrate the working of the decision tree based algorithm.
3. Build an artificial neural network by implementing the back propagation algorithm and test the same using appropriate data sets.
4. Write a program to implement the naive Bayesian classifier.

Unsupervised learning

1. Implementing neural network using self-organizing maps
2. Implementing k-Means algorithm to cluster a set of data.
3. Implementing hierarchical clustering algorithm.

Note:

• Installation of gnu-prolog, Study of Prolog (gnu-prolog).
• The programs can be implemented in using C++/JAVA/ Python or appropriate tools can be used by designing good user interface
• Data sets can be taken from standard repositories
• (https://archive.ics.uci.edu/ml/datasets.html) or constructed by the students.

Course Outcomes:

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Text Books:

1. S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach”, Prentice Hall, Fourth Edition, 2021
2. S.N.Sivanandam and S.N.Deepa, Principles of soft computing-Wiley India.3 rd ed,

Reference Books:

1. Machine Learning. Tom Mitchell. First Edition, McGraw- Hill, 1997.
2. I. Bratko, “Prolog: Programming for Artificial Intelligence!, Fourth edition, Addison-Wesley Educational Publishers Inc., 2011.
3. C. Muller & Sarah Alpaydin, Ethem. Introduction to machine learning. MIT press, 2020.

For detailed syllabus of all the other subjects of Pharmacy 6th Sem, visit Pharma 6th Sem subject syllabuses for 2021 regulation.

For all Pharmacy results, visit Anna University Pharma all semester results direct link.