Artificial Intelligence & Intelligent Systems
Complete Artificial Intelligence Masterclass
From Classical AI to Artificial General Intelligence Research
Published October 2025
Flexible course duration
Duration depends on the student's background and pace. Beginners (kids / teens): typically 6 to 9 months. Adults with prior knowledge: often shorter, with an accelerated path.
Standard pace6 to 9 months
AcceleratedAdd class frequency to finish faster
For personalised duration planning, call +91 91233 66161 and we'll map a schedule to your goals.
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Program Overview
This is not just an AI course, it's a complete journey through the entire landscape of Artificial Intelligence. Whether you're a complete beginner, computer science student, researcher, or professional wanting deep AI expertise, this 12-month masterclass will transform you into an AI expert with comprehensive knowledge spanning from classical symbolic AI to modern neural approaches, from theoretical foundations to practical intelligent systems.
You'll master AI from philosophical foundations to cutting-edge research: from search algorithms to knowledge representation, from expert systems to autonomous agents, from logic and reasoning to probabilistic AI, from planning to learning, from single agents to multi-agent systems, from narrow AI to AGI concepts. By the end, you'll have built 50+ intelligent systems, understood AI at the deepest level, published research, and be prepared for AI research scientist roles or PhD programs.
What Makes This Program Different
- Comprehensive coverage: Classical + Modern AI
- Theoretical foundations AND practical implementation
- Covers all AI paradigms: symbolic, connectionist, hybrid
- Philosophy and ethics integrated throughout
- Research methodology and paper writing
- Mathematics for AI taught rigorously
- Robotics and embodied AI included
- Multi-agent systems and game theory
- Cognitive architectures and human-level AI
- AGI (Artificial General Intelligence) concepts
- Industry applications across all domains
- 50+ intelligent systems built from scratch
- Preparation for AI research careers and PhD
Your Learning Journey
Phase 1
Foundations (Months 1-3): AI History, Search, Logic, Knowledge Representation, Problem Solving
Phase 2
Reasoning & Learning (Months 4-6): Probabilistic AI, Planning, Reasoning Under Uncertainty, ML Integration
Phase 3
Intelligent Systems (Months 7-9): Expert Systems, Agents, Robotics, Vision, NLP, Multi-Agent Systems
Phase 4
Advanced AI (Months 10-12): Cognitive AI, AGI, Research, Ethics, Philosophy, Emerging Frontiers
Career Progression
1
AI Research Engineer (after 3 months)
2
AI Systems Architect (after 6 months)
3
Senior AI Researcher (after 9 months)
4
AI Research Scientist / PhD Ready (after 12 months)
Detailed Course Curriculum
Explore the complete week-by-week breakdown of what you'll learn in this comprehensive program.
Topics Covered
- What is Artificial Intelligence? Definitions and perspectives
- History of AI: from Dartmouth Conference to present
- AI winters and AI springs
- Turing Test and philosophical foundations
- Strong AI vs Weak AI
- Narrow AI, General AI (AGI), Super AI
- Symbolic AI vs Connectionist AI vs Hybrid AI
- AI paradigms: logic-based, knowledge-based, learning-based
- Rational agents and intelligent behavior
- PEAS framework: Performance, Environment, Actuators, Sensors
Projects You Build
- Simple reflex agent implementation
- Model-based agent for grid world
- Agent environment simulator
- AI timeline and evolution visualization
- Turing Test chatbot (rule-based)
Practice & Assignments
Study 20 landmark AI papers from history
Topics Covered
- Problem-solving as search
- State space representation
- Problem formulation: initial state, actions, goal test, path cost
- Tree search vs graph search
- Search strategies evaluation: completeness, optimality, time, space
- Breadth-First Search (BFS): algorithm and analysis
- Uniform Cost Search (UCS)
- Depth-First Search (DFS): algorithm and analysis
- Depth-Limited Search (DLS)
- Iterative Deepening DFS (IDDFS)
Projects You Build
- 8-puzzle solver with BFS/DFS
- Maze solver with multiple algorithms
- Route finding system (city maps)
- N-Queens problem solver
- Missionaries and Cannibals problem
- Search visualization tool
- Performance comparison framework
Practice & Assignments
Implement all uninformed search algorithms from scratch
Topics Covered
- Heuristic functions and admissibility
- Greedy Best-First Search
- A* Search: algorithm, optimality proof
- Heuristic design and dominance
- Memory-bounded heuristic search: IDA*, RBFS, SMA*
- Weighted A* and bounded suboptimal search
- Bidirectional heuristic search
- Pattern databases for heuristics
- Local search algorithms
- Hill-climbing and variants
Projects You Build
- A* pathfinding implementation
- 15-puzzle solver with pattern databases
- Route optimization with A*
- Simulated annealing for TSP
- Genetic algorithm for optimization
- Hill-climbing variants comparison
- Heuristic function designer tool
Practice & Assignments
Solve 30 search problems with optimal algorithms
Topics Covered
- Game theory basics for AI
- Game trees and game search
- Minimax algorithm
- Alpha-beta pruning
- Move ordering for better pruning
- Iterative deepening in games
- Evaluation functions for game states
- Quiescence search
- Transposition tables
- Monte Carlo Tree Search (MCTS)
Projects You Build
- Tic-Tac-Toe with minimax
- Chess AI with alpha-beta pruning
- Checkers AI implementation
- Connect-4 game AI
- Go AI with MCTS (simplified)
- Poker bot (partial information)
- Game AI framework with multiple algorithms
Practice & Assignments
Build 10 different game-playing AI agents
Topics Covered
- Knowledge-based agents
- Logic as knowledge representation
- Propositional logic: syntax and semantics
- Logical connectives: AND, OR, NOT, IMPLIES, IFF
- Truth tables and logical equivalence
- Inference in propositional logic
- Inference rules: Modus Ponens, And-Elimination
- Resolution and refutation
- Conjunctive Normal Form (CNF)
- Horn clauses and forward/backward chaining
Projects You Build
- Propositional logic inference engine
- SAT solver implementation
- Logic-based puzzle solver (Sudoku)
- Wumpus World agent with logic
- Circuit verification system
- Resolution theorem prover
- Knowledge base system
Practice & Assignments
Solve 40 logical reasoning problems
Topics Covered
- First-Order Logic (FOL) syntax
- Predicates, functions, quantifiers
- Universal and existential quantification
- Semantics and interpretation
- Using FOL for knowledge representation
- Unification algorithm
- Generalized Modus Ponens
- Forward chaining in FOL
- Backward chaining in FOL
- Resolution in FOL
Projects You Build
- FOL inference engine
- Prolog-like system in Python
- Automated theorem prover
- Knowledge base with FOL
- Family tree reasoning system
- Medical diagnosis system (logic-based)
- Natural language to FOL translator (simple)
Practice & Assignments
Build 10 knowledge-based systems with FOL
Topics Covered
- CSP framework: variables, domains, constraints
- Examples: map coloring, N-Queens, scheduling
- Backtracking search for CSP
- Variable ordering heuristics: MRV, degree heuristic
- Value ordering: least-constraining-value
- Inference in CSP: forward checking
- Arc consistency (AC-3 algorithm)
- Path consistency and k-consistency
- Local search for CSP: min-conflicts
- Constraint propagation
Projects You Build
- N-Queens CSP solver
- Sudoku solver with constraint propagation
- Map coloring problem
- Course scheduling system
- Resource allocation optimizer
- Timetable generator
- PHASE 1 MINI CAPSTONE: Intelligent Planning System with CSP
Assessment
Phase 1 Exam - Search, Logic, Knowledge Representation, CSP
Topics Covered
- Uncertainty in AI systems
- Probability basics: events, axioms, rules
- Conditional probability and Bayes' Rule
- Bayesian reasoning
- Random variables: discrete and continuous
- Probability distributions
- Joint probability distributions
- Independence and conditional independence
- Inference using full joint distributions
- Marginalization and conditioning
Projects You Build
- Bayesian inference engine
- Naive Bayes spam filter
- Medical diagnosis system (probabilistic)
- Weather prediction system
- Probabilistic reasoning toolkit
- Bayesian calculator and visualizer
Practice & Assignments
Solve 50 probabilistic reasoning problems
Topics Covered
- Bayesian Network structure and semantics
- Conditional independence in Bayes nets
- Constructing Bayesian networks
- Compact conditional distributions: CPTs
- Exact inference in Bayesian networks
- Inference by enumeration
- Variable elimination algorithm
- Clustering algorithms for inference
- Junction tree algorithm
- Approximate inference: sampling methods
Projects You Build
- Bayesian Network inference engine
- Variable elimination implementation
- MCMC sampler for Bayes nets
- Medical diagnosis Bayes net
- Risk assessment system
- Bayes net learning from data
- Decision support system with Bayes nets
Practice & Assignments
Build 15 Bayesian network applications
Topics Covered
- Time and uncertainty
- Markov processes and Markov assumption
- Hidden Markov Models (HMM)
- HMM representation and semantics
- Forward algorithm for filtering
- Viterbi algorithm for most likely sequence
- Forward-backward algorithm for smoothing
- Baum-Welch algorithm for learning HMMs
- Applications: speech recognition, POS tagging
- Kalman filters for continuous state
Projects You Build
- HMM for speech recognition (simplified)
- POS tagger with HMM
- Robot localization with Kalman filter
- Particle filter for tracking
- Weather prediction with HMM
- Stock market HMM (simplified)
- Dynamic Bayesian network system
Practice & Assignments
Implement all temporal reasoning algorithms
Topics Covered
- Making decisions under uncertainty
- Utility theory and preferences
- Utility functions and axioms
- Maximum Expected Utility (MEU)
- Decision networks (influence diagrams)
- Value of information
- Value of perfect information (VPI)
- Sequential decision problems
- Markov Decision Processes (MDPs)
- MDP formulation: states, actions, transitions, rewards
Projects You Build
- Decision network solver
- MDP solver (value iteration, policy iteration)
- Grid world MDP
- Inventory management with MDPs
- Robot navigation with POMDP
- Medical treatment decision system
- Resource allocation optimizer
Practice & Assignments
Solve 25 decision-theoretic problems
Topics Covered
- Planning with probabilistic effects
- Contingency planning
- Conditional plans and execution monitoring
- Replanning strategies
- Online planning and acting
- Information gathering actions
- Exploration vs exploitation
- Multi-armed bandit problems
- UCB algorithms for exploration
- Monte Carlo planning
- Rollout algorithms
- Hierarchical planning under uncertainty
- Applications: robotics, autonomous systems
Projects You Build
- Contingent planner
- Multi-armed bandit solver
- Robot exploration system
- Adaptive planning agent
- Online planning framework
- Monte Carlo planner
Practice & Assignments
Build 10 planning under uncertainty systems
Topics Covered
- Planning problem representation
- STRIPS representation
- Actions: preconditions and effects
- State-space search for planning
- Forward (progression) state-space search
- Backward (regression) state-space search
- Heuristics for planning: ignore-preconditions, ignore-delete-lists
- Plan-space planning (partial-order planning)
- Graphplan algorithm
- Planning graphs and graph heuristics
Projects You Build
- STRIPS planner implementation
- Graphplan algorithm
- Blocks world planner
- Logistics planning system
- Robot task planner
- SAT-based planner
- HTN planner (simplified)
Practice & Assignments
Solve 20 classical planning problems
Topics Covered
- Knowledge engineering process
- Ontology definition and components
- Upper ontologies: Cyc, SUMO
- OWL (Web Ontology Language)
- RDF and semantic web
- Knowledge graphs
- Ontology design patterns
- Domain modeling
- Taxonomies and hierarchies
- Relationships and properties
Projects You Build
- Domain ontology creation (healthcare/e-commerce)
- Knowledge graph builder
- Ontology-based reasoning system
- Semantic search engine
- Question answering with knowledge graphs
- Ontology alignment tool
- SPARQL query system
Practice & Assignments
Build 5 ontologies for different domains
Topics Covered
- Learning agents architecture
- Supervised learning integration in AI
- Decision tree learning: ID3, C4.5
- Information gain and entropy
- Ensemble learning in AI
- Unsupervised learning for AI: clustering
- Reinforcement learning fundamentals (AI perspective)
- Q-learning in AI agents
- Temporal difference learning
- Exploration strategies
Projects You Build
- Decision tree AI agent
- Reinforcement learning grid world
- Q-learning robot navigator
- Learning agent framework
- Hybrid reasoning system (logic + learning)
- Transfer learning AI agent
- Meta-learning experiment
Practice & Assignments
Integrate learning into 15 AI systems
Topics Covered
- Explainable AI (XAI) importance
- Explanation in expert systems
- Trace-based explanations
- Rule-based explanation generation
- Argumentation and justification
- Counterfactual explanations
- Contrastive explanations
- Transparency vs performance tradeoffs
- LIME for model explanations
- SHAP values in AI
Projects You Build
- Explainable expert system
- Explanation generator for decisions
- Argumentation framework
- Counterfactual explanation system
- XAI dashboard
- Causal reasoning engine
- Interactive explanation interface
Practice & Assignments
Add explanations to all AI systems built
Topics Covered
- Integrated AI system design
- Combining multiple AI techniques
- Reasoning under uncertainty
- Planning and decision-making
- Learning and adaptation
- Explanation generation
Projects You Build
- PHASE 2 CAPSTONE: Intelligent Decision Support System
- Requirements: Bayesian reasoning, planning, decision theory, learning, explanations
- Option 1: Medical diagnosis and treatment planning system
- Option 2: Autonomous robot mission planner
- Option 3: Smart home energy management system
- Option 4: Financial portfolio advisor with uncertainty
Assessment
Phase 2 Exam - Probabilistic AI, Planning, Decision Theory, Learning
Topics Covered
- Expert systems overview and history
- MYCIN, DENDRAL, XCON case studies
- Expert system architecture: knowledge base, inference engine, UI
- Rule-based expert systems
- Forward chaining systems
- Backward chaining systems
- Conflict resolution strategies
- Certainty factors and fuzzy logic
- Dempster-Shafer theory
- Knowledge acquisition bottleneck
Projects You Build
- Medical diagnosis expert system
- Legal reasoning expert system
- Technical troubleshooting system
- Financial advisory expert system
- Expert system shell development
- Rule-based configurator
- Fuzzy logic controller
Practice & Assignments
Build 10 domain-specific expert systems
Topics Covered
- Agent theory and design
- Deliberative agents (BDI: Belief-Desire-Intention)
- Reactive agents and subsumption architecture
- Hybrid agent architectures
- Layered architectures: InteRRaP, TouringMachines
- Agent communication languages (ACL)
- FIPA specifications
- Agent reasoning cycles
- Agent planning and execution
- Agent learning and adaptation
Projects You Build
- BDI agent implementation
- Reactive agent for dynamic environment
- Hybrid agent architecture
- Communicating agents system
- Personal assistant agent
- Agent-based automation system
- Situated agent in simulation
Practice & Assignments
Design and implement 12 intelligent agents
Topics Covered
- AI in robotics overview
- Robot perception: sensors and processing
- Computer vision for robotics
- Object recognition and tracking
- SLAM (Simultaneous Localization and Mapping)
- Robot motion planning: configuration space
- Path planning algorithms for robots
- Potential field methods
- Probabilistic roadmaps (PRM)
- Rapidly-exploring Random Trees (RRT)
Projects You Build
- Robot simulator with AI planning
- Path planning visualizer (PRM, RRT)
- SLAM implementation (simplified)
- Behavior-based robot controller
- Robot learning from demonstration
- Autonomous navigation system
- ROS-based AI agent (if feasible)
Practice & Assignments
Implement 10 robotics AI algorithms
Topics Covered
- Vision as AI problem
- Image formation and camera models
- Image processing fundamentals
- Edge detection: Canny, Sobel
- Feature detection: SIFT, SURF, ORB
- Image segmentation techniques
- Object recognition classical methods
- Template matching
- Hough transform for shape detection
- Optical flow and motion analysis
Projects You Build
- Object detector (classical methods)
- Face recognition system
- Motion tracking application
- Image segmentation tool
- 3D reconstruction (simple)
- Visual reasoning system
- Autonomous vehicle perception (simulated)
Practice & Assignments
Build 12 computer vision AI applications
Topics Covered
- Language as AI challenge
- Syntax: parsing and grammars
- Context-free grammars
- Parsing algorithms: CKY, Earley
- Semantic analysis and representation
- First-order logic for semantics
- Lambda calculus for meaning
- Discourse and pragmatics
- Coreference resolution
- Information extraction
Projects You Build
- Parser for natural language
- Semantic representation generator
- Question answering system (logic-based)
- Dialogue system with state management
- Information extraction system
- Natural language interface to database
- Story understanding system
Practice & Assignments
Build 10 NLU systems with AI techniques
Topics Covered
- Multi-agent systems (MAS) introduction
- Agent interactions and coordination
- Cooperation, collaboration, competition
- Agent communication protocols
- Speech acts and performatives
- Contract Net Protocol
- Negotiation and bargaining
- Auction mechanisms
- Coalition formation
- Task allocation in MAS
Projects You Build
- Multi-agent coordination system
- Negotiation protocol implementation
- Auction system with agents
- Coalition formation simulator
- Distributed task allocation
- Multi-agent marketplace
- Swarm intelligence application
Practice & Assignments
Implement 12 multi-agent scenarios
Topics Covered
- Game theory foundations
- Normal form games
- Nash equilibrium concept
- Finding Nash equilibria
- Dominant strategies
- Mixed strategies
- Extensive form games
- Subgame perfect equilibrium
- Repeated games and folk theorem
- Evolutionary game theory
Projects You Build
- Game theory solver (Nash equilibrium)
- Repeated game simulator
- Auction mechanism with game theory
- Voting system analyzer
- Cooperative game solver
- Mechanism design implementation
- Multi-agent game scenarios
Practice & Assignments
Analyze 20 game-theoretic scenarios
Topics Covered
- Swarm intelligence principles
- Ant Colony Optimization (ACO)
- Particle Swarm Optimization (PSO)
- Bee algorithms
- Flocking and bird behavior (Boids)
- Stigmergy and indirect communication
- Self-organization in swarms
- Emergent intelligence
- Distributed sensing and actuation
- Robot swarms
Projects You Build
- Ant Colony Optimization for TSP
- Particle Swarm Optimizer
- Flocking simulation (Boids)
- Robot swarm simulator
- Stigmergy-based system
- Cellular automata explorer
- Artificial life simulation
Practice & Assignments
Implement 8 swarm intelligence algorithms
Topics Covered
- Social intelligence in AI
- Theory of Mind in AI
- Emotion recognition and affective computing
- Sentiment analysis for AI
- Social robots and companions
- Conversational AI and chatbots
- Personality in AI agents
- Trust and transparency in AI
- Anthropomorphism and uncanny valley
- Human-in-the-loop AI systems
Projects You Build
- Emotion-aware chatbot
- Social robot behavior system
- Theory of Mind agent
- Personality-based AI assistant
- Collaborative AI system
- Human-AI teaming framework
- Culturally-aware AI agent
Practice & Assignments
Build 10 social AI applications
Topics Covered
- Complex multi-agent system design
- Intelligent agent development
- Coordination and cooperation
- Real-world application
- Evaluation and analysis
Projects You Build
- PHASE 3 CAPSTONE: Intelligent Multi-Agent System
- Requirements: Multiple agents, coordination, learning, real-world application
- Option 1: Smart city traffic management with agents
- Option 2: Multi-robot warehouse automation
- Option 3: Distributed energy grid management
- Option 4: Multi-agent trading and auction system
- Option 5: Disaster response coordination system
Assessment
Phase 3 Exam - Agents, Robotics, Vision, NLP, Multi-Agent Systems
Topics Covered
- What are cognitive architectures?
- Human cognition and AI
- Symbolic cognitive architectures: SOAR
- ACT-R architecture
- CLARION: hybrid architecture
- Connectionist architectures
- Global Workspace Theory
- Memory systems: working, episodic, semantic
- Attention mechanisms in cognition
- Meta-cognition and self-awareness
Projects You Build
- SOAR agent implementation
- ACT-R cognitive model
- Memory system simulation
- Attention-based cognitive agent
- Metacognitive reasoning system
- Cognitive architecture comparison
- Human performance simulator
Practice & Assignments
Implement 5 cognitive architecture models
Topics Covered
- Narrow AI vs AGI distinction
- Definitions of intelligence
- General intelligence requirements
- Transfer learning and generalization
- Common sense reasoning
- Abstraction and analogy
- Causal reasoning for AGI
- AGI architectures and proposals
- OpenCog framework
- AIXI theoretical model
Projects You Build
- Common sense reasoning system
- Analogy-making engine
- Causal reasoning framework
- Transfer learning across domains
- AGI architecture proposal (theoretical)
- General problem solver (modern attempt)
- AGI evaluation framework
Practice & Assignments
Study and critique 10 AGI proposals
Topics Covered
- Philosophy of AI: can machines think?
- Chinese Room argument (Searle)
- Computational theory of mind
- Functionalism vs physicalism
- Consciousness in machines
- Hard problem of consciousness
- Qualia and subjective experience
- Intentionality in AI
- Free will and determinism
- Personal identity and AI
Projects You Build
- Philosophical argument analyzer
- Consciousness detection framework (theoretical)
- Turing Test variants implementation
- Theory of Mind test suite
- Ethical reasoning system for AI consciousness
- Philosophy paper comparative analysis
Practice & Assignments
Write 5 philosophical position papers on AI
Topics Covered
- AI safety research overview
- Alignment problem: aligning AI with human values
- Value learning from humans
- Inverse reinforcement learning for alignment
- Cooperative Inverse Reinforcement Learning (CIRL)
- Corrigibility and interruptibility
- Off-switch problem
- Reward hacking and specification gaming
- Robustness and adversarial examples
- Interpretability for safety
Projects You Build
- Value learning system
- Inverse RL for preference learning
- Corrigible AI agent
- Adversarial robustness tester
- AI safety evaluation framework
- Alignment research simulation
- Safety verification tools
Practice & Assignments
Analyze 20 AI safety scenarios
Topics Covered
- Ethics in AI development and deployment
- Bias and fairness in AI systems
- Algorithmic bias detection and mitigation
- Accountability and transparency
- Privacy in AI systems
- Surveillance and AI
- AI in criminal justice: risks and concerns
- Employment and automation
- Economic impact of AI
- AI governance and regulation
Projects You Build
- Bias detection toolkit for AI
- Fairness metrics implementation
- Ethical AI decision framework
- Privacy-preserving AI system
- AI governance policy analyzer
- Ethical AI certification checklist
- Societal impact assessment tool
Practice & Assignments
Conduct ethics review of all previous projects
Topics Covered
- Conducting AI research
- Literature review and survey methodology
- Research question formulation
- Hypothesis testing in AI
- Experimental design for AI research
- Benchmarks and datasets
- Evaluation metrics for AI systems
- Statistical significance in AI experiments
- Reproducibility and replicability
- Research ethics and integrity
Projects You Build
- Literature review on chosen AI topic
- Research proposal writing
- Experimental AI study design
- Research paper writing
- Conference presentation preparation
- Reproducible research package
- Open science publication
Practice & Assignments
Read 50 research papers, write 3 paper summaries
Topics Covered
- Neural-symbolic integration
- Neuro-symbolic AI approaches
- Knowledge-guided machine learning
- Causal AI and causal inference
- Few-shot and zero-shot learning
- Meta-learning (learning to learn)
- Continual and lifelong learning
- Compositional generalization
- Grounded language learning
- Embodied AI and embodied cognition
Projects You Build
- Neuro-symbolic system implementation
- Causal reasoning AI experiment
- Meta-learning framework
- Continual learning system
- Compositional generalization study
- World model for simple environment
- Research experiment in chosen cutting-edge area
Practice & Assignments
Implement 5 cutting-edge research ideas
Topics Covered
- AI in healthcare: diagnosis, drug discovery, personalized medicine
- AI in finance: algorithmic trading, fraud detection, risk assessment
- AI in education: adaptive learning, tutoring systems
- AI in transportation: autonomous vehicles, traffic optimization
- AI in manufacturing: predictive maintenance, quality control
- AI in agriculture: precision farming, crop monitoring
- AI in energy: smart grids, optimization
- AI in entertainment: games, content generation
- AI in e-commerce: recommendations, search, chatbots
- AI in cybersecurity: threat detection, response
Projects You Build
- Industry-specific AI system (choose domain)
- Healthcare AI application
- Financial AI tool
- Educational AI tutor
- Industrial AI solution
- Case study analysis (5 industries)
- AI product strategy document
Practice & Assignments
Design AI solutions for 10 different industries
Topics Covered
- AI research scientist roles
- Academic vs industry research
- PhD programs in AI: top universities
- Research internships and fellowships
- AI research labs: Google AI, DeepMind, OpenAI, FAIR, Microsoft Research
- Building research portfolio
- Publications and citations
- Research proposals and grant writing
- Collaboration and networking in research
- Interdisciplinary AI research
Projects You Build
- Research portfolio website
- PhD application materials
- Research statement writing
- Grant proposal (practice)
- Academic CV for AI research
- Research collaboration plan
- Career development roadmap
Practice & Assignments
Prepare complete PhD application package
Topics Covered
- AI trends and predictions
- Scaling laws and large models
- Foundation models and their impact
- Multimodal AI systems
- AI and quantum computing convergence
- AI and neuroscience synergy
- Artificial life and open-ended evolution
- AI and human augmentation
- Brain-computer interfaces
- Digital immortality and mind uploading
Projects You Build
- Future of AI analysis report
- Technology forecasting study
- Transformative AI scenario planning
- Long-term AI governance proposal
- Personal AI research vision
- Contribution plan to AI field
Practice & Assignments
Write position paper on future of AI
Topics Covered
- Identifying research gaps
- Novel contribution definition
- System architecture design
- Integration of multiple AI paradigms
- Theoretical foundations
- Innovation and creativity in AI
Projects You Build
- Original AI system proposal
- Architecture design document
- Theoretical framework
- Innovation justification
- Related work analysis
Practice & Assignments
Design completely novel AI approach
Topics Covered
- Prototype implementation
- Baseline comparisons
- Ablation studies
- Performance evaluation
- Debugging and refinement
- Scalability analysis
Projects You Build
- Working AI system implementation
- Experimental evaluation
- Performance benchmarking
- Comparison with state-of-art
- Error analysis
Practice & Assignments
Iterative development and testing
Topics Covered
- Research paper writing
- Code documentation
- Reproducibility package
- Visualization and presentation
- Submission preparation
- Open-source release
Projects You Build
- Complete research paper
- Supplementary materials
- Code repository with docs
- Presentation slides
- Demo video
- Blog post for wider audience
Practice & Assignments
Professional research communication
Topics Covered
- Project presentation
- Defending research decisions
- Future work identification
- Career positioning
- Continued research planning
Projects You Build
- FINAL CAPSTONE: Original AI Research Contribution
- Requirements: Novel AI system, theoretical contribution, extensive evaluation, publication-ready
- Examples:
- - Novel cognitive architecture for general reasoning
- - Hybrid neuro-symbolic system for causal reasoning
- - Multi-agent system with emergent communication
- - AI safety mechanism for value alignment
- - General problem-solving framework
- - Explainable AI system with human-like reasoning
- - Continual learning architecture with knowledge retention
Assessment
FINAL COMPREHENSIVE EXAM + THESIS DEFENSE - Complete AI mastery evaluation
Projects You'll Build
Build a professional portfolio with 130+ intelligent systems across all AI paradigms real-world projects.
Phase 1 30+ foundational AI projects - search, logic, CSP, games
Phase 2 35+ probabilistic and planning projects - Bayes nets, MDPs, planning systems
Phase 3 40+ intelligent systems - expert systems, agents, robotics, multi-agent
Phase 4 25+ advanced projects - cognitive AI, AGI research, ethics, novel systems
Total 130+ AI projects from classical to cutting-edge
Weekly Learning Structure
Theory Study
6-8 hours (lectures, papers, textbooks)
Implementation
10-12 hours (coding, system building)
Projects
5-7 hours (applying concepts)
Research Reading
3-4 hours (papers, surveys)
Experimentation
2-3 hours (testing, analysis)
Total Per Week
25-30 hours
Certification & Recognition
Phase Certificates
Certificate after each phase (4 total)
Final Certificate
Advanced Artificial Intelligence Certification
Specialization Tracks
Choose: Cognitive AI / Multi-Agent Systems / AGI Research / AI Safety
Research Certificate
Research publication certification (with paper acceptance)
Thesis Completion
Master's-level thesis equivalent
Portfolio
50+ documented AI systems
Publications
Support for publishing 1-3 papers
Digital Credentials
Blockchain-verified credentials
Industry Recognized
Recognized by top AI research labs
Phd Ready
Preparation equivalent to first year PhD coursework
Technologies & Skills You'll Master
Comprehensive coverage of the entire modern web development stack.
Classical AI
Search algorithms, game playing, constraint satisfaction, logic, knowledge representation
Probabilistic AI
Bayesian networks, HMMs, Kalman filters, DBNs, probabilistic reasoning
Planning
STRIPS, Graphplan, HTN, temporal planning, planning under uncertainty
Decision Theory
MDPs, POMDPs, decision networks, utility theory, game theory
Learning
Decision trees, RL (Q-learning, TD), hybrid symbolic-neural systems
Expert Systems
Rule-based systems, forward/backward chaining, explanation generation
Agents
BDI architecture, reactive agents, multi-agent systems, agent communication
Robotics
SLAM, path planning, motion control, robot perception
Vision
Classical computer vision, object recognition, scene understanding
NLP
Parsing, semantic analysis, question answering, dialogue systems
Multi-Agent
Coordination, negotiation, auctions, swarm intelligence, game theory
Cognitive AI
Cognitive architectures (SOAR, ACT-R), memory systems, metacognition
AGI
Common sense reasoning, causal reasoning, transfer learning, general intelligence
Philosophy
Mind theory, consciousness, ethics, AI safety, alignment
Research
Paper reading, writing, experimentation, publication, innovation
Tools
Python (expert), Prolog, knowledge representation languages, simulation environments
Support & Resources
Expert Mentorship
PhD-level AI researchers as mentors
Research Guidance
Help with research direction and paper writing
Paper Reading Groups
Weekly paper discussions
Project Reviews
Expert feedback on all implementations
Thesis Advising
Support for capstone/thesis project
Phd Preparation
Guidance for PhD applications
Community
Active researcher community
Resources
Access to papers, textbooks, datasets
Computing
Cloud credits for experiments
Conferences
Guidance on conference submissions
Collaboration
Research collaboration opportunities
Lifetime Access
All materials, updates, and advanced modules
Career Outcomes & Opportunities
Transform your career with industry-ready skills and job placement support.
Prerequisites
Education
Bachelor's degree in CS/Engineering/Math/Physics recommended (not required)
Mathematics
Comfort with calculus, linear algebra, probability (will be taught/reviewed)
Programming
Basic programming knowledge helpful (Python taught from scratch for AI)
Logic
Basic logical thinking (formal logic taught in course)
Commitment
Very high - this is research-level training
Time
25-30 hours/week consistently for 12 months
Mindset
Intellectual curiosity, love of theory + practice, research orientation
Equipment
High-performance computer recommended, cloud computing provided
Reading
Comfort reading academic papers and textbooks
Writing
Academic writing skills (developed during course)
Who Is This Course For?
Phd Aspirants
Preparing for AI/CS PhD programs at top universities
Researchers
Want comprehensive AI research training
CS Students
Advanced undergrad or master's students in computer science
AI Professionals
Industry professionals wanting deep theoretical knowledge
Career Switchers
STEM backgrounds transitioning to AI research
Academics
Faculty wanting to add AI research to their portfolio
Engineers
Want to move from applied ML to AI research
Innovators
Want to develop novel AI systems and algorithms
Philosophers
Interest in AI, mind, consciousness, ethics
Visionaries
Want to contribute to AGI development
Serious Learners
Committed to mastering AI at the deepest level
Career Paths After Completion
AI Research Scientist (Industry labs: Google AI, DeepMind, OpenAI, FAIR, etc.)
Research Engineer (Top tech companies)
PhD Student (Top AI programs: Stanford, MIT, CMU, Berkeley, Oxford, etc.)
Postdoctoral Researcher
University Professor (with PhD)
AI Research Group Leader
Chief AI Scientist / CTO
AI Safety Researcher
Cognitive Systems Architect
AGI Researcher
AI Ethics Researcher
Multi-Agent Systems Specialist
Robotics AI Researcher
AI Consultant (Advanced)
AI Startup Founder (Research-driven)
Salary & Market Context
The ranges below are general market salary bands for these roles in India and abroad, drawn from public industry data. They are shown for career context only and are not a promise or guarantee of income. Actual pay depends on your skills, experience, location, and the job market.
Other Roles
₹15-30 LPA (India), $120k-200k (USA); ₹25-50 LPA (India), $180k-350k (USA); ₹40-80 LPA (India), $250k-500k (USA); ₹60-150 LPA (India), $350k-800k+ (USA); DeepMind, OpenAI pay $300k-$1M+ for top researchers; ₹10-30 LPA (India), $80k-150k (USA) as Assistant Professor
Consulting
₹200-500/hour for expert AI consulting
Course Guarantees
Live Classes
Live, interactive classes with a real instructor, never pre-recorded videos.
Small Batches
Small batches only: group classes are capped at 10 students, with mini-batch (3 to 4 students) and personal 1-on-1 options.
Structured Curriculum
A structured, well-paced curriculum taught step by step, with hands-on practice in every session.
Doubt Support
Doubt support between classes over WhatsApp, so you are never left stuck.
Certificate
A course-completion certificate you can share.
Free Demo
A free demo class before you enrol, so you can decide with no pressure.
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What families say
Straight from the parents and students who learn with us. Rated 4.9 across 547 Google reviews.
“Mivaan enjoys the class. He understands the concepts and completes his tasks with excitement. He started taking interest in coding… truly amazing class.”
“I absolutely love it here! I made new friends and learned important valuable coding skills while having the fun of my life. It's not just coding here, it's outings, bonding and most importantly preparing you for your future. Definitely five stars.”
“What stands out most is how excited my son is before every class—he looks forward to learning, problem-solving, and sharing what he's built. I've noticed a big boost in his confidence!”
“Modern Age Coders has been a game-changer for me! I struggled to grasp IT concepts and coding before joining, but their classes transformed everything. I'm now the topper in my class and can confidently write complex programs with ease.”
“Modern Age Coder have wonderful teachers who teach in a clear, easy and practical way. The teacher boosts students' confidence, keeps them updated with technology, and inspires them to learn without hesitation.”
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“Coding classes here make learning very interesting and conceptual. The teachers teach us in a very easy-to-understand and efficient manner.”
“One of the most wonderful education centres out there. Education is not limited to school syllabus but focuses on skill development. Learning here has been a wonderful journey and still continuing.”
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“My child Dhairya is really enjoying the Modern Age Coder IT classes. This is his first online class, and he eagerly looks forward to it.”
“Very good classes. Don't worry about coding—they teach the best, especially Shivam sir.”
“Very good classes. Makes learning very easy and interactive.”
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