SYLLABUS
Unit I: Introduction: Towards Intelligent Machines Well posed Problems, Example of Applications in
diverse fields, Data Representation, Domain Knowledge for Productive use of Machine Learning,
Diversity of Data: Structured / Unstructured, Forms of Learning, Machine Learning and Data Mining,
Basic Linear Algebra in Machine Learning Techniques.
Unit II: Supervised Learning: Rationale and Basics: Learning from Observations, Bias and Why
Learning Works: Computational Learning Theory, Occam's Razor Principle and Over fitting Avoidance
Heuristic Search in inductive Learning, Estimating Generalization Errors, Metrics for assessing
regression, Metris for assessing classification.
Unit III: Statistical Learning: Machine Learning and Inferential Statistical Analysis, Descriptive
Statistics in learning techniques, Bayesian Reasoning: A probabilistic approach to inference, K-Nearest
Neighbor Classifier. Discriminant functions and regression functions, Linear Regression with Least
Square Error Criterion, Logistic Regression for Classification Tasks, Fisher's Linear Discriminant and
Thresholding for Classification, Minimum Description Length Principle.
Unit IV: Support Vector Machines (SVM): Introduction, Linear Discriminant Functions for Binary
Classification, Perceptron Algorithm, Large Margin Classifier for linearly seperable data, Linear Soft
Margin Classifier for Overlapping Classes, Kernel Induced Feature Spaces, Nonlinear Classifier, and
Regression by Support vector Machines.
Learning with Neural Networks: Towards Cognitive Machine, Neuron Models, Network Architectures,
Perceptrons, Linear neuron and the Widrow-Hoff Learning Rule, The error correction delta rule.
Unit V: Multilayer Perceptron Networks and error back propagation algorithm, Radial Basis Functions
Networks. Decision Tree Learning: Introduction, Example of classification decision tree, measures of
impurity for evaluating splits in decision trees, ID3, C4.5, and CART decision trees, pruning the tree,
strengths and weakness of decision tree approach.
What is Machine Learning ?
Machines learn from their past experiences and machines follow instructions given by humans but what if humans can train the machines to learn from the past data and do what humans can do and much faster well that's called machine learning but it's a lot more than just learning it's also about understanding and reasoning so today we will learn about the basics of machine learning.
supervised learning uses labeled data to train the machine and then there is reinforcement learning which is a reward based learning or we can say that it works on the principle of feedback to generalize machine learning model. The learning with unlabeled data is unsupervised learning so we saw supervised learning where the data was labeled. Siri asks how is machine learning possible in today's era well that's because today we have humongous data available everybody is online either making a transaction or just surfing the internet.
There are a lot of applications of machine learning out there to name a few machine learning is used in healthcare where diagnostics are predicted for doctor's review and fraud detection in the finance sector.
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