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Savitribai Phule Pune University, Maharashtra (SPPU), Mechanical Engineering Semester 6, Artificial Intelligence & Machine Learning Syllabus
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Unit - 1 Introduction to AI & ML
Unit 1
Introduction to AI ML
1.1 History of AI
1.2 Comparison of AI with Data Science
1.3 Need of AI in Mechanical Engineering
1.4 Introduction to Machine Learning
1.5 Basics Reasoning problem solving Knowledge representation Planning Learning Perception Motion and manipulation
1.6 Approaches to AI Cybernetics and brain simulation Symbolic Subsymbolic Statistical
1.7 Approaches to ML Supervised learning Unsupervised learning Reinforcement learning
Unit - 2 Feature Extraction and Selection
Unit 2
Feature Extraction and Selection
2.1 Feature extraction Statistical features
2.2 Principal Component Analysis
2.3 Feature selection Ranking
2.4 Decision tree Entropy reduction and information gain Exhaustive
2.5 Best first
2.6 Greedy forward backward
2.7 Applications of feature extraction and selection algorithms in Mechanical Engineering
Unit - 3 Classification & Regression
Unit 3
Classification Regression
3.1 Classification Decision tree Random forest
3.2 Naive Bayes
3.3 Support vector machine
3.4 Regression Logistic Regression
3.5 Support Vector Regression
3.6 Regression trees Decision tree random forest
3.7 KMeans
3.8 KNearest Neighbor KNN
3.9 Applications of classification and regression algorithms in Mechanical Engineering
Unit - 4 Development of ML Model
Unit 4
Development of ML Model
4.1 Problem identification classification
4.2 Clustering
4.3 Regression
4.4 Ranking
4.5 Steps in ML modeling
4.6 Data Collection
4.7 Data preprocessing
4.8 Model Selection
4.9 Model training Training Testing Kfold Cross Validation
4.10 Model evaluation understanding and interpretation of confusion matrix Accuracy Precision Recall True positive false positive etc.
4.11 Hyper parameter Tuning
4.12 Predictions
Unit - 5 Reinforced and Deep Learning
Unit 5
Reinforced and Deep Learning
5.1 Characteristics of reinforced learning
5.2 Algorithms Value Based Policy Based Model Based
5.3 Positive vs Negative Reinforced Learning
5.4 Models Markov Decision Process Q Learning
5.5 Characteristics of Deep Learning
5.6 Artificial Neural Network
5.7 Convolution Neural Network
5.8 Application of Reinforced and Deep Learning in Mechanical Engineering
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