Course

Unit-1 AI Introduction Perceptron, Multi-Layer perceptron, Markov Decision Process, Logical Agent & First Order Logic, AL Applications Unit-2 Supervised & Unsupervised Learning Introduction of Machine Learning, Supervised Learning, Linear Regression , Linear Equation ,Slope ,Intercept ,R square value , Logistic regression , ODDS ratio, Probability of success, Probability of failure Bias Variance Tradeoff , ROC curve, Bias Variance Tradeoff, K-Means , K-Means ++ , Hierarchical Clustering. Unit-3 Other Machine Learning Algorithms K Nearest Neighbour, Nave Bayes Classifier, Decision Tree CART, Decision Tree C50, Random Forest. Unit-4 Deep Learning Deep Learning Algorithms, CNN Convolutional Neural Network, RNN Recurrent Neural Network, ANN Artificial Neural Network Unit-5 Applications and Future Challenges, AI in Chemistry, Drug Discovery, Space Applications, Geoinformatics, Defence etc.