What AI topics are covered in the Artificial Intelligence Masterclass?

The course covers search and logic concepts, machine learning, neural networks, CNNs, RNNs, GANs, fuzzy systems, NLP, swarm intelligence, multi-agent systems, and AI ethics.

How does this course explain the difference between CNNs and RNNs?

It introduces CNNs for image-based tasks such as classification and RNNs for sequential data such as stock-price prediction, with practical deep-learning sessions.

Does the course include generative AI and multi-agent systems?

Yes. It includes generative AI, conditional GANs, agent negotiation, and swarm intelligence, alongside core machine-learning and deep-learning concepts.

Free Course Image Artificial Intelligence Masterclass: Search, Logic, Machine Learning and Deep Learning

Free online course Artificial Intelligence Masterclass: Search, Logic, Machine Learning and Deep Learning

Duration of the online course: 31 hours and 57 minutes

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Build in-demand AI skills with a free online course covering search, logic, machine learning and deep learning, plus exercises to earn a certificate-ready edge.

In this free course, learn about

Multi-Agent Systems, Swarm Intelligence, and AI Ethics
Natural Language Processing and Fuzzy Systems
Generative AI and Deep Learning Practice
Convolutional and Recurrent Neural Networks
Neural Networks and Hypothesis Learning
Machine Learning Algorithms and Ensemble Methods
Decision Theory and Probabilistic Reasoning
Knowledge Representation and Non-Monotonic Reasoning
Planning, Scheduling, and Expert Systems
Logic, Inference, and Knowledge-Based Agents
Genetic Algorithms and Genetic Programming
Adversarial Search and Game Playing
Informed and Uninformed Search Strategies
AI Foundations, Agents, and Course Introduction

About the free online course

Artificial intelligence is no longer a niche topic: it shapes products, decisions, and the way organizations compete. This free online course helps you build a solid, job-relevant foundation by connecting the classic roots of AI with the methods behind today’s most widely used systems. Instead of treating topics in isolation, you will learn how different AI approaches fit together, when each one is appropriate, and how to reason about their strengths and trade-offs.

You will start by understanding what it means for a system to behave intelligently through the lens of agents, environments, and rational decision-making. From there, you will develop a practical intuition for problem formulation and algorithmic thinking by exploring search, including strategies that balance completeness, optimality, and resource limits. This focus is valuable not only for AI, but also for everyday engineering problem solving: defining states, actions, and goals clearly is a transferable skill.

As you progress, the course moves into game-playing and adversarial reasoning, then broadens to evolutionary computation and genetic approaches, giving you tools for exploring large solution spaces when exact methods are costly. You will also build a rigorous understanding of knowledge-based systems, logical representation, and inference, learning how machines can store facts, express relationships, and derive conclusions in a principled way.

Modern AI depends on uncertainty-aware reasoning, so you will learn how probabilistic models support decision-making in the real world. You will connect probability and utility to decision theory, Markov processes, and sequential decision problems, creating a clear pathway to learning systems that improve with data. The machine learning portion develops core ideas such as classification, decision trees, ensembles, and neural networks, followed by deep learning concepts and practical perspectives on datasets and representation. You will also gain exposure to generative AI and complementary areas including fuzzy systems, NLP, ethics, swarm intelligence, and multi-agent coordination.

Throughout the course, exercises help you verify understanding and turn theory into confidence. By the end, you will be able to speak the language of AI, choose appropriate techniques for common scenarios, and prepare for further study or applied projects in artificial intelligence and machine learning.

Course content

Video class: Lec 58 Multi agent systems 34m
Exercise: In a multi-agent system, which description best matches negotiation between agents?
Video class: Lec 57 Swarm Intelligence 35m
Exercise: In swarm intelligence, which statement best explains how complex global behavior arises?
Video class: Lec 56 Ethics in AI 39m
Exercise: Which set of principles is highlighted as key aspects of AI ethics?

Video class: Lec 55 Natural Language Processing 32m
Exercise: Which type of ambiguity occurs when a single word has multiple meanings (e.g., bank as a financial institution or river edge)?
Video class: Lec 54 Fuzzification and Defuzzification methods 34m
Exercise: In fuzzy set theory, what does the core of a membership function represent?
Video class: Lec 53 Fuzzy Sets and Systems 29m
Exercise: In fuzzy set theory, how is the union of two fuzzy sets A and B typically computed for an element x?

Video class: Lec 52 Generative AI 36m
Video class: Lec 51 Practical Section - 5 29m
Exercise: In a many-to-one RNN setup for stock prediction, what does the model output represent?
Video class: Lec 50 Practical Section - 4 25m
Exercise: In a 4-class CNN image classification task using Keras, which loss function and output activation are most appropriate?
Video class: Lec 49 Practical session - 3 1h08m
Exercise: In image representation for CNNs, how many channels are typically used for an RGB (color) image?

Video class: Lec 48 RNN 34m
Exercise: In a conditional GAN (cGAN), what additional input is provided to both the generator and the discriminator to enable targeted generation?
Video class: Lec 47 CNN and introduction to RNN 41m
Exercise: In a CNN, what is the main purpose of pooling (e.g., max pooling or average pooling) after convolution?
Video class: Lec 46 Datasets for DL 30m
Exercise: Which dataset is described as having 14 million labeled images spanning more than 2,000 categories?
Video class: Lec 45 Introduction to deep learning 32m
Exercise: In deep learning, what is a key advantage regarding feature engineering compared to standard machine learning?

Video class: Lec 44 Practical session - 2 58m
Exercise: In a binary classification neural network, which combination is appropriate for the output layer and loss function?
Video class: Lec 43 Practical session - 1 42m
Exercise: In a multiclass classification problem with 3 classes, what output-layer setup is appropriate for an MLP?
Video class: Lec 42 Hypothesis Learning 43m
Exercise: In current best hypothesis learning, what update is performed when a new training example is a false negative for the current hypothesis?
Video class: Lec 41 Multilayer Perceptron Neural network 19m
Video class: Lec 40 Introduction to Neural Networks 30m
Exercise: In training an artificial neural network, what is primarily updated to make the network produce the desired output for a given input?

Video class: Lec 39 Ensemble Learning 25m
Exercise: In ensemble classification, what does the simplest and most robust method use to combine outputs from multiple classifiers?
Video class: Lec 38 Learning Algorithms 31m
Exercise: When constructing a decision tree, how is the attribute for a node typically chosen?
Video class: Lec 37 Learning Algorithms 35m
Exercise: In the K-nearest neighbors (KNN) classifier, how is a new pattern P typically assigned a class label?
Video class: Lec 36 Introduction to Learning 34m
Exercise: Which option correctly matches a learning type with the kind of feedback it uses?

Video class: Lec 35 Decision Theory - 2 30m
Exercise: Which set of components correctly defines a Markov Decision Process (MDP)?
Video class: Lec 34 Decision Theory - 1 28m
Exercise: In multiattribute decision making, what does it mean if option S1 has strict dominance over option S2?
Video class: Lec 33 Utility Theory 31m
Exercise: How does a rational agent choose an action according to the Maximum Expected Utility (MEU) principle?
Video class: Lec 32 Dynamic belief networks (DBN) 33m
Exercise: In a first-order Markov process used in a Dynamic Bayesian Network, which dependency is assumed for the transition model?
Video class: Lec 31 Bayesian Network 26m
Exercise: In a Bayesian network, what does a directed arrow from node X to node Y represent?
Video class: Lec 30 Uncertanity in Knowledge Representation 38m
Exercise: In a decision-theoretic agent, how is the final action typically chosen under uncertainty?

Video class: Lec 29 Representation Schemes - 2 30m
Exercise: In a conceptual graph, what do the circular/elliptical nodes represent?
Video class: Lec 28 Representation Schemes - 1 32m
Exercise: In a semantic network, how does the inheritance algorithm typically determine a property value (e.g., number of legs) for a specific object?
Video class: Lec 27 Non Monotonic Reasoning 26m
Exercise: In non-monotonic reasoning, what does a Truth Maintenance System (TMS) help an agent do when a fact is retracted?
Video class: Lec 26 Knowledge Representation 29m
Exercise: In knowledge representation using categories, what does it mean when two categories are disjoint?

Video class: Lec 25 Planning and Scheduling 27m
Exercise: In scheduling after planning, what identifies the critical path in a plan?
Video class: Lec 24 Planning Graph 40m
Exercise: In a planning graph, when is the graph typically terminated (stopped from further expansion)?
Video class: Lec 23 Introduction to Planning 31m
Exercise: In planning using PDDL, what is typically included in an action schema?
Video class: Lec 22 Applications of Expert Systems 20m
Exercise: In an expert system, which components are typically kept separate so that different inference methods can operate on the same knowledge?
Video class: Lec 21 Introduction to Expert Systems 31m
Exercise: In an expert system, what is the primary role of the inference engine?

Video class: Lec 20 Inference in First Order Logic 33m
Exercise: Which technique speeds up forward chaining by only considering rules where at least one premise fact was newly inferred in the previous iteration?
Video class: Lec 19 Inference in Predicate Logic 33m
Exercise: In predicate-logic resolution, what does deriving nil (empty clause) after adding the negation of the goal to the knowledge base indicate?
Video class: Lec 18 Predicate Logic - 2 32m
Exercise: In first-order logic for the family-relationships domain, how is a grandparent relationship correctly characterized?
Video class: Lec 17 Predicate Logic - 1 30m
Exercise: Which statement best captures a key advantage of predicate (first-order) logic over propositional logic?
Video class: Lec 16 Proposotional Logic 32m
Exercise: In propositional logic, which statement correctly describes the connective “X implies Y” (X → Y)?
Video class: Lec 15 Knowledge-Base Agents 30m
Exercise: In a knowledge-based agent, which pair best describes what is stored in the knowledge base (KB)?

Video class: Lec 14 Genetic Programming 29m
Exercise: In genetic programming, how is crossover typically performed on program representations?
Video class: Lec 13 GA Operators 32m
Exercise: In roulette-wheel (stochastic sampling with replacement) selection in a genetic algorithm, how is the chance of a string being chosen determined?
Video class: Lec 12 Introduction to Genetic Algorithms 31m
Exercise: In local search, which statement best describes what matters when moving through the solution space?

Video class: Lec 11 Game-Playing Search 29m
Exercise: In alpha-beta pruning, what do the parameters α (alpha) and β (beta) represent?
Video class: Lec 10 Adversarial or Game-Playing Search 31m
Exercise: In the minimax procedure for two-player zero-sum games, how are values backed up from children to a parent node?

Video class: Lec 09 Informed Search Strategies - II 29m
Exercise: In memory-bounded A* (e.g., SMA*), what action is taken when available memory is exhausted during search?
Video class: Lec 08 Informed Search Strategies - I 29m
Exercise: In the A* (A-star) search algorithm, how is the evaluation function f(n) defined?
Video class: Lec 07 Uninformed Search Strategies - II 31m
Exercise: What is the key idea behind Iterative Deepening Depth-First Search (IDDFS)?
Video class: Lec 06 Uninformed Search Strategies - I 26m
Exercise: In Breadth-First Search (BFS), which node is selected for expansion at each step?
Video class: Lec 05 Introduction to Search Strategies 28m
Exercise: In graph search (compared to tree search), what additional check prevents generating redundant paths?

Video class: Lec 04 General Problem Solving 34m
Exercise: In a search-based problem formulation, which component specifies the successor state reached by applying an action in a given state?
Video class: Lec 03 Agents and Environment 35m
Exercise: Which statement best defines a rational agent?
Video class: Lec 02 History 32m
Exercise: What key limitation made early neural networks less effective before hidden layers became popular again in the 1980s?
Video class: Lec 01 Course overview 30m
Exercise: Which set of capabilities was listed as necessary for a computer system to pass the Turing test?
Video class: AI Concepts and Techniques Intro 08m
Exercise: Which topic is introduced as the starting point of the course overview?

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