Artificial Intelligence

Birla Institute Of Technology And Science–Pilani (BITS–Pilani) via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

10 weeks, 5 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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Welcome to the exciting world of Artificial Intelligence! In this course, you will explore the fundamentals of artificial intelligence and gain valuable insights that drive decision-making in today's AI-driven world. Whether you're a budding AI scientist, an AI Consultant looking to enhance your core AI skills, or simply curious about the breadth and depth of AI fundamentals, this course is your gateway to mastering the core of Artificial Intelligence. This course provides a comprehensive introduction to artificial intelligence with a strong emphasis on real-world applications and practical problem-solving. Students will learn core AI techniques through hands-on projects that address contemporary challenges in healthcare, robotics, finance, gaming, autonomous systems, and intelligent web applications. The course combines theoretical foundations with extensive practical implementation, focusing on how AI systems are deployed in industry and research. Students will work with real datasets, build intelligent applications, and develop solutions to authentic problems using search algorithms, optimisation techniques, knowledge-based systems, probabilistic reasoning, and reinforcement learning approaches. You will also have the opportunity to collaborate with peers, fostering a supportive learning community. By the end of this Artificial Intelligence course, you will have developed a strong foundation in AI technologies and applications, enabling you to design intelligent systems with confidence. Whether you are looking to advance your career, leverage AI-driven solutions in your current role, or explore the vast opportunities in AI innovation, this course will empower you with the essential skills to succeed and contribute to the rapidly evolving AI landscape.

Syllabus

Introduction to AI and Intelligent Agents
- This module provides a comprehensive introduction to artificial intelligence, covering its definition, importance, key components, and industry applications. Students will learn to analyse the business problems and identify the relationship between the agent and the environment. They will be able to identify the PEAS (Performance Measure, Environment, Actuator, Sensor) specifications of the task environment. Additionally, the students will gain an understanding of different agent architectures and be able to relate to various real-world applications.
Problem Solving by Searching
- This module focuses on problem solving through classical search algorithms in observable, deterministic, known environments where the solution is a sequence of actions. The module begins with formally defining the problem formulation for the environment and defining the search techniques. Students will be able to understand, apply and evaluate solutions based on classical search techniques like Depth First Search, Breadth First Search and Uniform Cost Search.
Game Playing
- This module focuses on exploring a gaming scenario through adversarial search techniques. The unpredictability of the other agents can introduce contingencies into the agent’s problem-solving process. Students will understand and explore the working of a competitive environment through Adversarial search techniques. They will be able to design solutions for such adversarial problems based on the min-max algorithm. Finally, they will also learn to optimise the memory efficiency of solutions through alpha-beta pruning.
Beyond the Classical Search
- This module focuses on exploring local and bio-inspired search techniques to deal with partially observable and unknown environments. Students will know when to use local search techniques and deal with sub-optimal solutions. They will also learn about evolutionary algorithms and apply them to solve real-world problems. Finally, they will be able to understand the working of swarm intelligence algorithms and their Stigmergy principles.
Knowledge Representation Using Propositional Logic
- This module explores the fundamental concepts of logical agents and knowledge representation using propositional logic, cornerstones of artificial intelligence (AI). Students can develop logic as a general class of representations to support knowledge-based agents. Such agents can combine and recombine information to suit myriad purposes. Students will learn about logical agents and formal reasoning to make decisions and infer new knowledge. They will also be able to create a simple knowledge base on PL and make inferences from it.
First Order Logic
- This module introduces the expressive power of First Order Logic (FOL) for representing relationships and reasoning about the world. You will learn its syntax and semantics, and how to perform inference using Propositionalisation and Forward Chaining. The module concludes with a real-world application where FOL is used to support knowledge-based reasoning systems.
Reasoning Under Uncertainty
- In this module, you will learn how to represent and reason with uncertain knowledge. You will be introduced to probability theory and how it is used in AI to model uncertainty. You will understand joint probability distributions, conditional independence, and the structure and semantics of Bayesian Networks.
Multiarmed Bandit
- This module introduces the classical reinforcement learning problem of the multiarmed bandit. You will learn how agents can make sequential decisions in an uncertain environment, balancing the trade-off between exploration and exploitation. The module also introduces the idea of regret minimisation, which quantifies the performance of a learning strategy over time. One real-world application of the multiarmed bandit will also be discussed.
Finite Markov Decision Processes
- This module introduces the formal framework of Markov Decision Processes (MDPs), which underlies much of reinforcement learning. Students will learn how to model sequential decision-making problems using MDPs by defining states, actions, transition probabilities, and rewards. The concepts of return and value functions will be explored in detail, along with the Bellman equations. The module concludes with an application of MDPs to robot navigation.
Temporal-Difference Learning
- This module introduces Temporal-Difference (TD) Learning, a fundamental idea in reinforcement learning where agents update their value estimates based on partial experience. Students will learn how value functions can be incrementally improved without requiring the full outcome of an episode. The module includes the key TD algorithms: TD(0), SARSA, and Q-learning. A final video shows how TD learning can be used to train an agent to play Tic-Tac-Toe or Snake, demonstrating its real-world applicability.