M.Tech I Year I Semester (R17)
Introduction - Well-posed learning problems, Designing a learning system, Perspectives and issues in machine learning Concept learning and the general to specific ordering – Introduction, A concept learning task, Concept learning as search, Find-S: finding a maximally specific hypothesis, Version spaces and the candidate elimination algorithm, Remarks on version spaces and candidate elimination, Inductive bias
Decision Tree learning – Introduction, Decision tree representation, Appropriate problems for decision tree learning, The basic decision tree learning algorithm, Hypothesis space search in decision tree learning, Inductive bias in decision tree learning, Issues in decision tree learning Artificial Neural Networks – Introduction, Neural network representation, Appropriate problems for neural network learning, Perceptions, Multilayer networks and the back propagation algorithm, Remarks on the back propagation algorithm, An illustrative example face recognition Advanced topics in artificial neural networks Evaluation Hypotheses – Motivation, Estimation hypothesis accuracy, Basics of sampling theory, A general approach for deriving confidence intervals, Difference in error of two hypotheses, Comparing learning algorithms
Bayesian learning – Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum likelihood and least squared error hypotheses, Maximum likelihood hypotheses for predicting probabilities, Minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve Bayes classifier, An example learning to classify text, Bayesian belief networks The EM algorithm Computational learning theory – Introduction, Probability learning an approximately correct hypothesis, Sample complexity for Finite Hypothesis Space, Sample Complexity for infinite Hypothesis Spaces, The mistake bound model of learning - Instance-Based Learning- Introduction, k -Nearest Neighbour Learning, Locally Weighted Regression, Radial Basis Functions, Case-Based Reasoning, Remarks on Lazy and Eager Learning Genetic Algorithms – Motivation, Genetic Algorithms, An illustrative Example, Hypothesis Space Search, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms
Learning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets: Summary, Learning First Order Rules, Learning Sets of First Order Rules: FOIL, Induction as Inverted Deduction, Inverting Resolution
Analytical Learning - Introduction, Learning with Perfect Domain Theories: Prolog-EBG Remarks on Explanation-Based Learning, Explanation-Based Learning of Search Control Knowledge
Combining Inductive and Analytical Learning – Motivation, Inductive-Analytical Approaches to Learning, Using Prior Knowledge to Initialize the Hypothesis, Using Prior Knowledge to Alter the Search Objective, Using Prior Knowledge to Augment Search Operators, Reinforcement Learning – Introduction, The Learning Task, Q Learning, Non-Deterministic, Rewards and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming
The Web Security Landscape: The Web Security Problem, Risk Analysis and Best Practices; Cryptography and the Web: Cryptography and Web Security, Working Cryptographic Systems and Protocols, What Cryptography Can’t Do? , Legal Restrictions on Cryptography.
The Web’s War on Your Privacy: Understanding Privacy, User-Provided Information, Log Files, Understanding Cookies, Web Bugs, Conclusion; Privacy-Protecting Techniques: Choosing a Good Service Provider, Picking a Great Password, Cleaning Up After Yourself, Avoiding Spam and Junk Email, Identity Theft; Privacy-Protecting Technologies: Blocking Ads and Crushing Cookies, Anonymous Browsing, Secure Email, Backups and Anti Theft: Using Backups to Protect Your Data, Preventing theft.
Physical security for Servers: Planning for the Forgotten Threats, Protecting Computer Hardware, Protecting Your Data, Host Security for Servers: Current Host Security Problems, Securing the Host Computer, minimizing Risk by Minimizing Services, Operating Securely, Secure Remote Access and Content Updating, firewalls and the Web, Securing Web Applications: A Legacy of Extensibility and Risk, Rules to Code By, Security Using Fields, Hidden Fields and Cookies, Rules for Programming languages, Using PHP Securely, Writing Scripts That Run with Additional Privileges, Connecting to Databases.
Deploying SSL Server Certificates: Planning for your SSL Server, Creating SSL Servers with FreeBSD, Installing an SSL Certificate on Microsoft IIS, Obtaining a Certificate from a Commercial CA, When Things Go Wrong; Securing Your Web Service: Protecting Via Redundancy, Protecting Your DNS, Protecting Your Domain Registration.
Controlling Access to Your Web Content: Access Control Strategies, Controlling Access with Apache, Controlling Access with Microsoft IIS; Client-Side Digital Certificates: Client Certificates, A Tour of the VeriSign Digital ID Center; Pornography, Filtering Software and Censorship: Pornography Filtering, PICS, RSAC, Privacy Policies, Legislation and P3P: Policies that Protect Privacy and Privacy Policies, Children’s Online Privacy Protection Act, P3P.
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