Intelligent Systems II
333 pages in the complete text edition.
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M2 · About
- Portion, source priority and what was checkedPortion, sources and corrections
- How inconsistent source questions are handledPortion, sources and corrections
M2 · About Chapter
M2 · Formulas
- Fuzzy formulas, with meanings and worked-example linksAll M2 formulas
- GA and PSO formulas, symbols and memory rulesAll M2 formulas
M2 · Formulas Chapter
M2 · Index
M2 · Mocks
- Q1(a) · Fuzzy operations [2]M2 mock 1 · 10 marks
- Q1(b) · Swarm intelligence [2]M2 mock 1 · 10 marks
- Q2 · GA through two generations [3]M2 mock 1 · 10 marks
- Q3 · Two-particle PSO through iteration 2 [3]M2 mock 1 · 10 marks
- Q4 · Fuzzy architecture [3]M2 mock 1 · 10 marks
- Q1(a) · Fuzzy operations [2]M2 mock 2 · 10 marks
- Q1(b) · Swarm intelligence [2]M2 mock 2 · 10 marks
- Q2 · GA through two generations [3]M2 mock 2 · 10 marks
- Q3 · Two-particle PSO through iteration 2 [3]M2 mock 2 · 10 marks
- Q4 · Fuzzy membership [3]M2 mock 2 · 10 marks
- Q1(a) · Fuzzy operations [2]M2 mock 3 · 10 marks
- Q1(b) · Swarm intelligence [2]M2 mock 3 · 10 marks
- Q2 · GA through two generations [3]M2 mock 3 · 10 marks
- Q3 · Two-particle PSO through iteration 2 [3]M2 mock 3 · 10 marks
- Q4 · Fuzzy defuzzification [3]M2 mock 3 · 10 marks
M2 · Mocks Chapter
M2 · Papers
- Q1(c) Fuzzy set operations [5]Final examination · 24 Nov 2025
- Q2(a) PSO, one iteration [10]Final examination · 24 Nov 2025
- Q3(b) Fuzzy loan-approval system [10]Final examination · 24 Nov 2025
- Q4(a) Cartesian product and max–min composition [10]Final examination · 24 Nov 2025
- Q5(b) Genetic algorithm [10]Final examination · 24 Nov 2025
- Q6(a) Applications of fuzzy logic [10]Final examination · 24 Nov 2025
- Q7(a) Applications of PSO [10]Final examination · 24 Nov 2025
- Q1(b) Applications of PSO [5]Re-examination · 31 Jan 2026
- Q1(c) Fuzzy set operations [5]Re-examination · 31 Jan 2026
- Q2(a) Fuzzy fire-hazard system [10]Re-examination · 31 Jan 2026
- Q3(b) Fuzzy relations and composition [10]Re-examination · 31 Jan 2026
- Q4(a) PSO, one iteration [10]Re-examination · 31 Jan 2026
- Q5(a) Genetic algorithm [10]Re-examination · 31 Jan 2026
- Q7(a) Applications of fuzzy logic [10]Re-examination · 31 Jan 2026
M2 · Papers Chapter
M2 · Read
- Start here: your IS2 M2 study orderStart here
- Symbols, brackets and iteration labels: how to read a formula1. The maths these three topics use
- Position, score, minimisation and maximisation1. The maths these three topics use
- Fractions, brackets, minimum and maximum1. The maths these three topics use
- Percentages, probabilities and cumulative intervals1. The maths these three topics use
- Binary from the beginning: positions, place values and bits1. The maths these three topics use
- Vectors, coordinates, matrices and transpose from the first entry1. The maths these three topics use
- Graph axes, intervals and the fractions inside membership functions1. The maths these three topics use
- Sum notation, ordinary averages and weighted averages1. The maths these three topics use
- Powers, roots, absolute values and exp on the calculator1. The maths these three topics use
- Fuzzy sets: what membership actually means2. Fuzzy logic, from meaning to full solutions
- Triangular membership: choose the side, then calculate2. Fuzzy logic, from meaning to full solutions
- Trapezoids, shoulders and intuition-based fuzzification2. Fuzzy logic, from meaning to full solutions
- Union, intersection, complement and difference2. Fuzzy logic, from meaning to full solutions
- Fuzzy properties, De Morgan laws and α-cuts2. Fuzzy logic, from meaning to full solutions
- Mamdani architecture: what every block does2. Fuzzy logic, from meaning to full solutions
- Rule firing, clipping and aggregation: four solved cases2. Fuzzy logic, from meaning to full solutions
- Mean of maxima: discrete peaks and clipped plateaus2. Fuzzy logic, from meaning to full solutions
- Centroid: three table sums and one area calculation2. Fuzzy logic, from meaning to full solutions
- Weighted average and centroid are not automatically the same2. Fuzzy logic, from meaning to full solutions
- Class fan question: 32°C, 7 people, every rule and step2. Fuzzy logic, from meaning to full solutions
- The same fan controller: true area centroid versus MoM and weighted average2. Fuzzy logic, from meaning to full solutions
- Fuzzy theory: definitions, architecture, advantages and applications2. Fuzzy logic, from meaning to full solutions
- Gaussian, singleton and bell memberships2. Fuzzy logic, from meaning to full solutions
- GA meaning, biological idea and every basic term3. Genetic algorithms
- Encoding: binary, real values and permutations3. Genetic algorithms
- Roulette selection, cumulative intervals and alternatives3. Genetic algorithms
- Crossover and mutation: four complete worked variants3. Genetic algorithms
- GA architecture, two-generation flow, constraints and theory3. Genetic algorithms
- Two generations with given selection, cut and mutation3. Genetic algorithms
- Two generations including roulette selection3. Genetic algorithms
- GA minimisation: transform cost before roulette selection3. Genetic algorithms
- Swarm intelligence, particles, velocity, pbest and gbest4. Particle swarm optimization
- Read the PSO equation and work three individual updates4. Particle swarm optimization
- Two particles, two iterations: start with the simplest minimisation4. Particle swarm optimization
- A worse move: pbest stays in the past4. Particle swarm optimization
- Two-dimensional PSO: keep the coordinates separate4. Particle swarm optimization
- Class PSO photograph: all three particles through iteration 24. Particle swarm optimization
- PSO algorithm, architecture, advantages and limitations4. Particle swarm optimization
- Cartesian product: every cell explainedFuzzy relations and matrices
- Max–min composition: three complete matrix examplesFuzzy relations and matrices
M2 · Read Chapter
M2 · Revision
- The final revision: methods, theory and common mistakesLast-day revision
M2 · Revision Chapter
TEE · About
- Portion, source priority and what was checkedPortion, sources and corrections
- How inconsistent source questions are handledPortion, sources and corrections
TEE · About Chapter
TEE · Formulas
- Fuzzy formulas, with meanings and worked-example linksAll M2 formulas
- GA and PSO formulas, symbols and memory rulesAll M2 formulas
- Unit 1 formulas: probability, Bayes, naive Bayes, Bayesian networks, GMMTEE formula sheet · Units 1 and 4
- Unit 4 formulas: Markov chains, MRP, MDP, BellmanTEE formula sheet · Units 1 and 4
- TEE-only formulas: ACO and GA for TSPTEE formula sheet · Units 1 and 4
TEE · Formulas Chapter
TEE · Index
TEE · Mocks
- Q1(a) Joint probability table [5]Final examination · 24 Nov 2025
- Q1(b) Applications of ACO [5]Final examination · 24 Nov 2025
- Q1(c) Fuzzy set operations [5]Final examination · 24 Nov 2025
- Q1(d) Brand-switching Markov chain [5]Final examination · 24 Nov 2025
- Q2(a) PSO, one iteration [10]Final examination · 24 Nov 2025
- Q2(b) GMM: compare with k-means, log-likelihood, parameters [10]Final examination · 24 Nov 2025
- Q3(a) Bayesian network: wet grass given no rain [10]Final examination · 24 Nov 2025
- Q3(b) Fuzzy loan-approval system [10]Final examination · 24 Nov 2025
- Q4(a) Cartesian product and max–min composition [10]Final examination · 24 Nov 2025
- Q4(b) Naive Bayes: flu [10]Final examination · 24 Nov 2025
- Q5(a) Hotel guest movement (Markov chain) [10]Final examination · 24 Nov 2025
- Q5(b) Genetic algorithm [10]Final examination · 24 Nov 2025
- Q6(a) Applications of fuzzy logic [10]Final examination · 24 Nov 2025
- Q6(b) Learner progression (Markov chain) [10]Final examination · 24 Nov 2025
- Q7(a) Applications of PSO [10]Final examination · 24 Nov 2025
- Q7(b) ACO for TSP [10]Final examination · 24 Nov 2025
- Q1(a) Disease, fever and test joint table [5]Re-examination · 31 Jan 2026
- Q1(b) Applications of PSO [5]Re-examination · 31 Jan 2026
- Q1(c) Fuzzy set operations [5]Re-examination · 31 Jan 2026
- Q1(d) Savings and current accounts (Markov chain) [5]Re-examination · 31 Jan 2026
- Q2(a) Fuzzy fire-hazard system [10]Re-examination · 31 Jan 2026
- Q2(b) GMM: compare with k-means, log-likelihood, parameters [10]Re-examination · 31 Jan 2026
- Q3(a) Bayesian network: fever given no flu [10]Re-examination · 31 Jan 2026
- Q3(b) Fuzzy relations and composition [10]Re-examination · 31 Jan 2026
- Q4(a) PSO, one iteration [10]Re-examination · 31 Jan 2026
- Q4(b) Naive Bayes: job offer [10]Re-examination · 31 Jan 2026
- Q5(a) Genetic algorithm [10]Re-examination · 31 Jan 2026
- Q5(b) Hotel booking channels (Markov chain) [10]Re-examination · 31 Jan 2026
- Q6(a) ACO for TSP [10]Re-examination · 31 Jan 2026
- Q6(b) Aircraft movement between hubs (Markov chain) [10]Re-examination · 31 Jan 2026
- Q7(a) Applications of fuzzy logic [10]Re-examination · 31 Jan 2026
- Q7(b) Bellman equation and applications of RL [10]Re-examination · 31 Jan 2026
TEE · Mocks Chapter
TEE · Papers
- Q1(a) Joint probability table [5]Final examination · 24 Nov 2025
- Q1(b) Applications of ACO [5]Final examination · 24 Nov 2025
- Q1(c) Fuzzy set operations [5]Final examination · 24 Nov 2025
- Q1(d) Brand-switching Markov chain [5]Final examination · 24 Nov 2025
- Q2(a) PSO, one iteration [10]Final examination · 24 Nov 2025
- Q2(b) GMM: compare with k-means, log-likelihood, parameters [10]Final examination · 24 Nov 2025
- Q3(a) Bayesian network: wet grass given no rain [10]Final examination · 24 Nov 2025
- Q3(b) Fuzzy loan-approval system [10]Final examination · 24 Nov 2025
- Q4(a) Cartesian product and max–min composition [10]Final examination · 24 Nov 2025
- Q4(b) Naive Bayes: flu [10]Final examination · 24 Nov 2025
- Q5(a) Hotel guest movement (Markov chain) [10]Final examination · 24 Nov 2025
- Q5(b) Genetic algorithm [10]Final examination · 24 Nov 2025
- Q6(a) Applications of fuzzy logic [10]Final examination · 24 Nov 2025
- Q6(b) Learner progression (Markov chain) [10]Final examination · 24 Nov 2025
- Q7(a) Applications of PSO [10]Final examination · 24 Nov 2025
- Q7(b) ACO for TSP [10]Final examination · 24 Nov 2025
- Q1(a) Disease, fever and test joint table [5]Re-examination · 31 Jan 2026
- Q1(b) Applications of PSO [5]Re-examination · 31 Jan 2026
- Q1(c) Fuzzy set operations [5]Re-examination · 31 Jan 2026
- Q1(d) Savings and current accounts (Markov chain) [5]Re-examination · 31 Jan 2026
- Q2(a) Fuzzy fire-hazard system [10]Re-examination · 31 Jan 2026
- Q2(b) GMM: compare with k-means, log-likelihood, parameters [10]Re-examination · 31 Jan 2026
- Q3(a) Bayesian network: fever given no flu [10]Re-examination · 31 Jan 2026
- Q3(b) Fuzzy relations and composition [10]Re-examination · 31 Jan 2026
- Q4(a) PSO, one iteration [10]Re-examination · 31 Jan 2026
- Q4(b) Naive Bayes: job offer [10]Re-examination · 31 Jan 2026
- Q5(a) Genetic algorithm [10]Re-examination · 31 Jan 2026
- Q5(b) Hotel booking channels (Markov chain) [10]Re-examination · 31 Jan 2026
- Q6(a) ACO for TSP [10]Re-examination · 31 Jan 2026
- Q6(b) Aircraft movement between hubs (Markov chain) [10]Re-examination · 31 Jan 2026
- Q7(a) Applications of fuzzy logic [10]Re-examination · 31 Jan 2026
- Q7(b) Bellman equation and applications of RL [10]Re-examination · 31 Jan 2026
- Q1(a) Joint masses, normalization and conditional probability [5]Final / re-examination · 28 Apr 2025
- Q1(b) Genetic algorithm applications [5]Final / re-examination · 28 Apr 2025
- Q1(c) Fuzzy union, intersection and complements [5]Final / re-examination · 28 Apr 2025
- Q1(d) Cable-company Markov matrix and diagram [5]Final / re-examination · 28 Apr 2025
- Q2(a) Fuzzy investment-controller design and centroid [10]Final / re-examination · 28 Apr 2025
- Q2(b) GMM versus k-means, log likelihood and parameter count [10]Final / re-examination · 28 Apr 2025
- Q3(a) Bayesian-network conditional cough probability [10]Final / re-examination · 28 Apr 2025
- Q3(b) Fuzzy Cartesian relations and composition-domain ambiguity [10]Final / re-examination · 28 Apr 2025
- Q4(a) PSO one iteration with missing initialization [10]Final / re-examination · 28 Apr 2025
- Q4(b) Naive Bayes product-purchase classification [10]Final / re-examination · 28 Apr 2025
- Q5(a) Binary GA crossover, one-bit mutation and fitness [10]Final / re-examination · 28 Apr 2025
- Q5(b) Bike-sharing Markov transitions, retention and gains [10]Final / re-examination · 28 Apr 2025
- Q6(a) ACO tour lengths, evaporation and pheromone deposition [10]Final / re-examination · 28 Apr 2025
- Q6(b) Two-step walking/cycling Markov transition [10]Final / re-examination · 28 Apr 2025
- Q7(a) Fuzzy logic applications and complete controller example [10]Final / re-examination · 28 Apr 2025
- Q7(b) Bellman equations and reinforcement-learning applications [10]Final / re-examination · 28 Apr 2025
- Q1(a) Traditional versus evolutionary optimization [5]Re-examination · 25 Jun 2024
- Q1(b) Bayes posterior for red ball [5]Re-examination · 25 Jun 2024
- Q1(c) Fuzzy union, intersection and complements [5]Re-examination · 25 Jun 2024
- Q1(d) Irreducibility and stationary distribution [5]Re-examination · 25 Jun 2024
- Q2(a) GMM modified log likelihood and parameters [10]Re-examination · 25 Jun 2024
- Q2(b) Fuzzy washing-machine controller [10]Re-examination · 25 Jun 2024
- Q3(a) Fuzzy relation and max-product comparison [10]Re-examination · 25 Jun 2024
- Q3(b) PSO maximization, supplied parameters [10]Re-examination · 25 Jun 2024
- Q4(a) Naive Bayes and Laplace smoothing [10]Re-examination · 25 Jun 2024
- Q4(b) Transition matrix properties and two-step probabilities [10]Re-examination · 25 Jun 2024
- Q5(a) GA selection, crossover and survivor replacement [10]Re-examination · 25 Jun 2024
- Q5(b) Mean of maxima and centroid of trapezoids [10]Re-examination · 25 Jun 2024
- Q6(a) Genetic algorithm for four-city TSP [10]Re-examination · 25 Jun 2024
- Q6(b) Markov process and homework transition paths [10]Re-examination · 25 Jun 2024
- Q7(a) Expectation Maximization algorithm [5]Re-examination · 25 Jun 2024
- Q7(b) Lambda-cut of a fuzzy relation [5]Re-examination · 25 Jun 2024
- Q7(c) ACO path length and pheromone update [5]Re-examination · 25 Jun 2024
- Q7(d) Bellman equation [5]Re-examination · 25 Jun 2024
- Q1(a) Conditional probability from fruit counts [5]Final examination · 17 Apr 2024
- Q1(b) Membership function and age sketch [5]Final examination · 17 Apr 2024
- Q1(c) Mutation operators [5]Final examination · 17 Apr 2024
- Q1(d) Markov chain diagram and path probability [5]Final examination · 17 Apr 2024
- Q2(a) Bayesian network joint probabilities [10]Final examination · 17 Apr 2024
- Q2(b) Fuzzy Cartesian product and composition [10]Final examination · 17 Apr 2024
- Q3(a) GMM versus K-means [10]Final examination · 17 Apr 2024
- Q3(b) Center of sums defuzzification [10]Final examination · 17 Apr 2024
- Q4(a) Fuzzy quality-control design [10]Final examination · 17 Apr 2024
- Q4(b) ACO components and roles [10]Final examination · 17 Apr 2024
- Q5(a) PSO inspiration, parameters and one iteration [10]Final examination · 17 Apr 2024
- Q5(b) Brand-switching transition matrix [10]Final examination · 17 Apr 2024
- Q6(a) GA ranking and three crossover methods [10]Final examination · 17 Apr 2024
- Q6(b) Markov property and weather path [10]Final examination · 17 Apr 2024
- Q7(a) Bayes bag posterior [5]Final examination · 17 Apr 2024
- Q7(b) Max–min relational composition [5]Final examination · 17 Apr 2024
- Q7(c) Point versus uniform crossover [5]Final examination · 17 Apr 2024
- Q7(d) Applications of reinforcement learning [5]Final examination · 17 Apr 2024
TEE · Papers Chapter
TEE · Read
- How the TEE is set and how to prepareStart here · TEE plan
- Every paper: question, unit, marks and solved answerStart here · TEE plan
- Symbols, brackets and iteration labels: how to read a formula1. The maths these three topics use
- Position, score, minimisation and maximisation1. The maths these three topics use
- Fractions, brackets, minimum and maximum1. The maths these three topics use
- Percentages, probabilities and cumulative intervals1. The maths these three topics use
- Binary from the beginning: positions, place values and bits1. The maths these three topics use
- Vectors, coordinates, matrices and transpose from the first entry1. The maths these three topics use
- Graph axes, intervals and the fractions inside membership functions1. The maths these three topics use
- Sum notation, ordinary averages and weighted averages1. The maths these three topics use
- Powers, roots, absolute values and exp on the calculator1. The maths these three topics use
- AND, OR and NOT with a pictureUnit 1 · Probability from zero (AND, OR, given, Bayes)
- “Given”: conditional probability by shrinking the worldUnit 1 · Probability from zero (AND, OR, given, Bayes)
- Bayes' rule with a tree of 1000 peopleUnit 1 · Probability from zero (AND, OR, given, Bayes)
- Why agents need probability (and the basic rules)Unit 1 · Uncertainty, joint distributions and Bayes rule
- Inference using the full joint distribution (5-mark PYQs)Unit 1 · Uncertainty, joint distributions and Bayes rule
- Bayes’ rule: meaning, derivation and three complete examplesUnit 1 · Uncertainty, joint distributions and Bayes rule
- The naive Bayes model (worked on PlayTennis)Unit 1 · Naive Bayes classifier
- Solved PYQ: naive Bayes for flu (Final 2025, 10 marks)Unit 1 · Naive Bayes classifier
- Solved PYQ: naive Bayes for a job offer (Re-exam 2026, 10 marks)Unit 1 · Naive Bayes classifier
- Bayesian networks: structure, CPTs and the chain ruleUnit 1 · Bayesian networks
- Inference in Bayesian networks: enumeration, α and variable eliminationUnit 1 · Bayesian networks
- Solved PYQ: wet grass given no rain (Final 2025, 10 marks)Unit 1 · Bayesian networks
- Solved PYQ: fever given no flu (Re-exam 2026, 10 marks)Unit 1 · Bayesian networks
- Before GMM: centre, spread, density and likelihoodUnit 1 · Gaussian mixture models and EM
- GMM prerequisites: mean, variance, density and mixtureUnit 1 · Gaussian mixture models and EM
- EM for GMM: every responsibility and parameter updateUnit 1 · Gaussian mixture models and EM
- 10-mark answer: GMM vs k-means, log-likelihood derivation, parameters for K = 5Unit 1 · Gaussian mixture models and EM
- Clustering with GMM: uses, advantages and limitsUnit 1 · Gaussian mixture models and EM
- Fuzzy sets: what membership actually means2. Fuzzy logic, from meaning to full solutions
- Triangular membership: choose the side, then calculate2. Fuzzy logic, from meaning to full solutions
- Trapezoids, shoulders and intuition-based fuzzification2. Fuzzy logic, from meaning to full solutions
- Union, intersection, complement and difference2. Fuzzy logic, from meaning to full solutions
- Fuzzy properties, De Morgan laws and α-cuts2. Fuzzy logic, from meaning to full solutions
- Mamdani architecture: what every block does2. Fuzzy logic, from meaning to full solutions
- Rule firing, clipping and aggregation: four solved cases2. Fuzzy logic, from meaning to full solutions
- Mean of maxima: discrete peaks and clipped plateaus2. Fuzzy logic, from meaning to full solutions
- Centroid: three table sums and one area calculation2. Fuzzy logic, from meaning to full solutions
- Weighted average and centroid are not automatically the same2. Fuzzy logic, from meaning to full solutions
- Class fan question: 32°C, 7 people, every rule and step2. Fuzzy logic, from meaning to full solutions
- The same fan controller: true area centroid versus MoM and weighted average2. Fuzzy logic, from meaning to full solutions
- Fuzzy theory: definitions, architecture, advantages and applications2. Fuzzy logic, from meaning to full solutions
- Gaussian, singleton and bell memberships2. Fuzzy logic, from meaning to full solutions
- Cartesian product: every cell explainedFuzzy relations and matrices
- Max–min composition: three complete matrix examplesFuzzy relations and matrices
- GA meaning, biological idea and every basic term3. Genetic algorithms
- Encoding: binary, real values and permutations3. Genetic algorithms
- Roulette selection, cumulative intervals and alternatives3. Genetic algorithms
- Crossover and mutation: four complete worked variants3. Genetic algorithms
- GA architecture, two-generation flow, constraints and theory3. Genetic algorithms
- Two generations with given selection, cut and mutation3. Genetic algorithms
- Two generations including roulette selection3. Genetic algorithms
- GA minimisation: transform cost before roulette selection3. Genetic algorithms
- GA approach to the travelling salesman problemGA for travelling salesman problem
- How pheromone feedback guides ant-colony searchUnit 3 · Ant colony optimization (ACO)
- ACO formulas: transition probability, city choice and pheromone updateUnit 3 · Ant colony optimization (ACO)
- Solved PYQ: ACO for a 4-city TSP (Final 2025, 10 marks)Unit 3 · Ant colony optimization (ACO)
- Solved PYQ: ACO for a 4-city TSP (Re-exam 2026, 10 marks)Unit 3 · Ant colony optimization (ACO)
- Practice: the lecture's Ant System example (two different tours)Unit 3 · Ant colony optimization (ACO)
- 5/10-mark answer: applications of ACO (explain one)Unit 3 · Ant colony optimization (ACO)
- Swarm intelligence, particles, velocity, pbest and gbest4. Particle swarm optimization
- Read the PSO equation and work three individual updates4. Particle swarm optimization
- Two particles, two iterations: start with the simplest minimisation4. Particle swarm optimization
- A worse move: pbest stays in the past4. Particle swarm optimization
- Two-dimensional PSO: keep the coordinates separate4. Particle swarm optimization
- Class PSO photograph: all three particles through iteration 24. Particle swarm optimization
- PSO algorithm, architecture, advantages and limitations4. Particle swarm optimization
- Reinforcement learning: learning from rewardsUnit 4 · What reinforcement learning is
- Markov property, transition matrix and state diagramUnit 4 · Markov processes (transition matrices)
- Next-step and n-step distributionsUnit 4 · Markov processes (transition matrices)
- Steady state and long-run gain or lossUnit 4 · Markov processes (transition matrices)
- Markov reward process, return and discountUnit 4 · MRP, MDP, policies and Bellman equations
- Bellman equation for an MRP (with a solved example)Unit 4 · MRP, MDP, policies and Bellman equations
- Markov decision process, policies and value functionsUnit 4 · MRP, MDP, policies and Bellman equations
- Bellman expectation and optimality equations (worked)Unit 4 · MRP, MDP, policies and Bellman equations
- Using Bellman equations: policy and value iterationUnit 4 · MRP, MDP, policies and Bellman equations
- 10-mark answer: Bellman equation and applications of RLUnit 4 · MRP, MDP, policies and Bellman equations
TEE · Read Chapter
- Start here · TEE plan
- 1. The maths these three topics use
- Unit 1 · Probability from zero (AND, OR, given, Bayes)
- Unit 1 · Uncertainty, joint distributions and Bayes rule
- Unit 1 · Naive Bayes classifier
- Unit 1 · Bayesian networks
- Unit 1 · Gaussian mixture models and EM
- 2. Fuzzy logic, from meaning to full solutions
- Fuzzy relations and matrices
- 3. Genetic algorithms
- GA for travelling salesman problem
- Unit 3 · Ant colony optimization (ACO)
- 4. Particle swarm optimization
- Unit 4 · What reinforcement learning is
- Unit 4 · Markov processes (transition matrices)
- Unit 4 · MRP, MDP, policies and Bellman equations
TEE · Revision
- The final revision: methods, theory and common mistakesLast-day revision
- Units 1 and 4 in one pageLast-day revision · TEE