Week 1 : Introduction: Philosophy of AI, Definitions
Week 2 : Modeling a Problem as Search Problem, Uninformed Search
Week 3 : Heuristic Search, Domain Relaxations
Week 4 : Local Search, Genetic Algorithms
Week 5 : Adversarial Search
Week 6 : Constraint Satisfaction
Week 7 : Propositional Logic & Satisfiability
Week 8 : Uncertainty in AI, Bayesian Networks
Week 9 : Bayesian Networks Learning & Inference, Decision Theory
Week 10: Markov Decision Processes
Week 11: Reinforcement Learning
Week 12:Introduction to Deep Learning & Deep RL
Course Status: Upcoming
Duration: 12 weeks
Start Date: 18 Jan 2021
End Date: 09 Apr 2021
Exam Date: 25 Apr 2021
Enrollment Ends: 25 Jan 2021
Category: Computer Science and Engineering
Level: Undergraduate/Postgraduate
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IIT Madras Invites free online Courses through NPTEL on Artificial Intelligence, Python for Data Science, Machine Learning and Data Science for Engineers.
National Programme on Technology Enhanced Learning (NPTEL), an initiative of seven Indian Institutes of Technology (IITs), has launched a series of free online course. And IIT Madras being one of the top free online course providers.
IIT Madras,
An intelligent agent needs to be able to solve problems in its world. The ability to create representations of the domain of interest and reason with these representations is a key to intelligence. In this course we explore a variety of representation formalisms and the associated algorithms for reasoning. We start with a simple language of propositions, and move on to first order logic, and then to representations for reasoning about action, change, situations, and about other agents in incomplete information situations. This course is a companion to the course “Artificial Intelligence: Search Methods for Problem Solving” that was offered recently and the lectures for which are available online.
This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD .We will assume that the students know programming for some of the assignments.If the students have done introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic topics in the first two weeks as well.
With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well-motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.
This course explores the 10 hrs of pre-course material will be provided, learners need to practise this to be ready to take the course.Interested participants can register on the official NPTEL website. The 12 week-long courses will be conducted from 18 January to 9 April 2021 and exam date 21 Mar 2021.
Python for Data Science
Python is
This course will take you from zero to programming in Python
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Indian Institute of Technology, Kanpur is offering two new free online courses on data science. This time they have offered it on SWAYAM NPTEL platform, which is a two-part course, as below:
In this course we will study how randomness helps in designing algorithms and how randomness can be removed from algorithms. We will start by formalizing computation in terms of algorithms and circuits. We will see an example of randomized algorithms-- identity testing --and prove that eliminating randomness would require proving hardness results. We prove hardness results for the problems of parity and clique using randomized methods. We construct `highly’-connected graphs called expanders that are useful in reducing randomness in algorithms. These lead to a surprising logarithmic-space algorithm for checking connectivity in graphs. We show that if there is hardness in nature then randomness cannot exist! This we prove by developing pseudo-random generators and error-correcting codes.
In this course we study mathematical techniques that enable us to show the power and limitations of various computational models. We consider these models by putting restrictions on the resources that the model can use and study the class of problems that are solvable by these models. We also compare the various classes that are thus obtained and try to give relations between them.
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IIT Bombay is currently inviting applications An Introduction to Programming Through C++,which can be taken by anyone who is interested to learn about the subject.The courses are available on the National Programme on Technology Enhanced Learning (NPTEL)
This course provides an introduction to problem solving and programming using the C++ programming language. The topics include:
- Basic programming notions. Control flow, variables and assignments statements, conditional execution, looping, function calls including recursion. Arrays and structures. Elementary aspects of classes. Heap memory.
- Program design. How human beings solve problems manually. Strategies for translating manual strategies to computer programs. Organizing large programs into units such as functions and classes. Introduction to assertions and invariants.
- Programming applications. Arithmetic on polynomials, matrices. Root finding. Sorting and searching. Design of editors and simulators, including graphical editors. Elementary animation. A rudimentary graphics system will be discussed.
- Standard Library of C++. The string, vector and map classes.
IIT Roorkee is currently inviting new free online courses on Data Analytics with Python.
Data Analytics with Python
By Prof. A Ramesh | IIT Roorkee
We are looking forward to sharing many exciting stories and examples of analytics with all of you using the python programming language. This course includes examples of analytics in a wide variety of industries, and we hope that students will learn how you can use analytics in their career and life. One of the most important aspects of this course is that you, the student, are getting hands-on experience creating analytics models; we, the course team, urge you to participate in the discussion forums and to use all the tools available to you while you are in the course!
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Cloud Computing and Distributed Systems
By Prof. Rajiv Misra | IIT Patna
Cloud computing is the on-demand delivery of computations, storage, applications, and other IT resources through a cloud services platform over the internet with pay-as-you-go business model. Today's Cloud computing systems are built using fundamental principles and models of distributed systems. This course provides an in-depth understanding of distributed computing “concepts”, distributed algorithms, and the techniques, that underlie today's cloud computing technologies. The cloud computing and distributed systems concepts and models covered in course includes: virtualization, cloud storage: key-value/NoSQL stores, cloud networking,fault-tolerance cloud using PAXOS, peer-to-peer systems, classical distributed algorithms such as leader election, time, ordering in distributed systems, distributed mutual exclusion, distributed algorithms for failures and recovery approaches, emerging areas of big data and many more. And while discussing the concepts and techniques, we will also look at aspects of industry systems such as Apache Spark, Google’s Chubby, Apache Zookeeper, HBase, MapReduce, Apache Cassandra, Google’s B4, Microsoft’s Swan and many others. Upon completing this course, students will have intimate knowledge about the internals of cloud computing and how the distributed systems concepts work inside clouds.
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Machine Learning, ML
By Prof. Carl Gustaf Jansson | KTH, The Royal Institute of Technology
The scientific discipline of Machine Learning focuses on developing algorithms to find patterns or make predictions from empirical data. It is a classical sub-discipline within Artificial Intelligence (AI). The discipline is increasingly used by many professions and industries to optimize processes and implement adaptive systems. The course places machine learning in its context within AI and gives an introduction to the most important core techniques such as decision tree based inductive learning, inductive logic programming, reinforcement learning and deep learning through decision trees.
HAPPY LEARNING!
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