Machine Learning Syllabus


MACHINE LEARNING


UNIT -I: The ingredients of machine learning, Tasks: the problems that can be solved with machine learning, Models: the output of machine learning, Features, the workhorses of machine learning. Binary classification and related tasks: Classification, Scoring and ranking, Class probability estimation

UNIT-II: Beyond binary classification: Handling more than two classes, Regression, Unsupervised and descriptive learning. Concept learning: The hypothesis space, Paths through the hypothesis space, Beyond conjunctive concepts

UNIT- III: Tree models: Decision trees, Ranking and probability estimation trees, Tree learning as variance reduction. Rule models: Learning ordered rule lists, Learning unordered rule sets, Descriptive rule learning, First-order rule learning

UNIT-IV: Linear models: The least-squares method, The perceptron: a heuristic learning algorithm for linear classifiers, Support vector machines, obtaining probabilities from linear classifiers, Going beyond linearity with kernel methods. Distance Based Models: Introduction,
Neighbours and exemplars, Nearest Neighbours classification, Distance-Based Clustering, Hierarchical Clustering.

UNIT- V: Probabilistic models: The normal distribution and it's geometric interpretations,
Probabilistic models for categorical data, Discriminative learning by optimising conditional
likelihoodProbabilistic models with hidden variables.Features: Kinds of feature, Feature transformations, Feature construction and selection. Model ensembles: Bagging and random forests, Boosting

UNIT- VI: Dimensionality Reduction: Principal Component Analysis (PCA), Implementation and demonstration. Artificial Neural Networks: Introduction, Neural network representation, appropriate problems for neural network learning, Multilayer networks and the back propagation algorithm.

TEXTBOOKS:
1. Machine Learning: The art andscience of algorithms that make sense of data, Peter Flach, Cambridge.
2. Machine Learning, Tom M.Mitchell, MGH.

REFERENCE BOOKS:
1. Understanding MachineLearning: From Theory to Algorithms, Shai Shalev-Shwartz, Shai Ben- David,Cambridge.
2. MachineLearning in Action, Peter Harington, 2012, Cengage.

Comments

Post a Comment

Popular posts from this blog

Tree pruning useful in decision tree induction

Why is tree pruning useful in decision tree induction? What is a drawback of using a separate set of tuples to evaluate pruning? The decision tree built may overfit the training data. There could be too many branches, some of which may reflect anomalies in the training data due to noise or outliers. Tree pruning addresses this issue of overfitting the data by removing the least reliable branches (using statistical measures). This generally results in a more compact and reliable decision tree that is faster and more accurate in its classification of data. The drawback of using a separate set of tuples to evaluate pruning is that it may not be representative of the training tuples used to create the original decision tree. If the separate set of tuples are skewed, then using them to evaluate the pruned tree would not be a good indicator of the pruned tree’s classification accuracy. Furthermore, using a separate set of tuples to evaluate pruning means there are less tuples to use for ...
Read more