COURSE OBJECTIVES:
The main objectives of this course are to:
Study about uninformed and Heuristic search techniques.
Learn techniques for reasoning under uncertainty
Introduce Machine Learning and supervised learning algorithms
Study about ensembling and unsupervised learning algorithms
Learn the basics of deep learning using neural networks
UNIT I PROBLEM SOLVING
Introduction to AI – AI Applications – Problem solving agents – search algorithms – uninformed
search strategies – Heuristic search strategies – Local search and optimization problems –
adversarial search – constraint satisfaction problems (CSP)
UNIT II PROBABILISTIC REASONING
Acting under uncertainty – Bayesian inference – naïve bayes models. Probabilistic reasoning –
Bayesian networks – exact inference in BN – approximate inference in BN – causal networks.
UNIT III SUPERVISED LEARNING
Introduction to machine learning – Linear Regression Models: Least squares, single & multiple
variables, Bayesian linear regression, gradient descent, Linear Classification Models: Discriminant
function – Probabilistic discriminative model – Logistic regression, Probabilistic generative model –
Naive Bayes, Maximum margin classifier – Support vector machine, Decision Tree, Random forests
UNIT IV ENSEMBLE TECHNIQUES AND UNSUPERVISED LEARNING
Combining multiple learners: Model combination schemes, Voting, Ensemble Learning – bagging,
boosting, stacking, Unsupervised learning: K-means, Instance Based Learning: KNN, Gaussian
mixture models and Expectation maximization
UNIT V NEURAL NETWORKS
Perceptron – Multilayer perceptron, activation functions, network training – gradient descent
optimization – stochastic gradient descent, error backpropagation, from shallow networks to deep
networks –Unit saturation (aka the vanishing gradient problem) – ReLU, hyperparameter tuning,
batch normalization, regularization, dropout.
UNIT 1
- uninformed search
- strategies
- Heuristic search strategies
- Local search and optimization problems
- adversaria l search
- constraint satisfaction problems (CSP)
UNIT II
- Bayesian inference
- naïve bayes models
- Bayesian networks
- exact inference in BN
- approximate inference in BN
UNIT III
Linear Regression Models: Least squares, single & multiple variables,
Bayesian linear regression, gradient descent
UNIT IV
Voting, Ensemble Learning – bagging,boosting, stacking,K-means,KNN
UNIT V
- Perceptron
- gradient descen optimization
- stochastic gradient descent, error backpropagation
- batchnormalization, regularization, dropout