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Some of the homework help topics include :
 
  • Design and implementation of intelligent systems,Different agent architectures,uninformed and heuristic search,local search and optimization,Constraint satisfaction problems,Game playing and adversarial search
  • Knowledge representation,Logical reasoning,Propositional logic,Planning algorithms,Reasoning under uncertainty,Bayes rule,Belief networks,Decision making,Utility theory
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Help for complex topics like :
 
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  • Constraints: interpreting line drawings,search, domain reduction ,visual object recognition ,learning, nearest neighbors
  • Learning: identification trees, disorder,neural nets, back propagation , genetic algorithms ,sparse spaces, phonology , near misses, felicity conditions, support vector machines boosting

Introduction to Artificial Intelligence

  • Representations: classes, trajectories, transitions
  • Architectures: GPS, SOAR, Subsumption, Society of Mind
  • The AI business
  • Probabilistic inference 
  • Model merging, cross-modal coupling, course summary
  • Scheme Review and Matching
  • Searches
  • Constraint Satisfaction
  • Games
  • Constraint Satisfaction Problems (CSP) and Games
  • Learning as Search
  • Formulating Search
  • Decision Trees
  • Naïve Bayes
  • Design Project Presentation and Question-Answer
  • Continuous Features
  • Naïve Bayes and Nearest Neighbor
  • Linear Separators
  • Neural Nets
  • Support Vector Machines (SVM)
  • Support Vector Machines (SVM)
  • Feature and Model Selection
  • Problem Set Review
  • Formulating Learning
  • Introduction to Logic and Representation
  • Propositional Logic
  • Natural Language Processing
  • Logic and Proof
  • First Order Logic
  • Syntax and Semantics
  • Rules
  • Language
  • Problem Set 
  • Language
  • Conclusion

Introduction to Artificial Intelligence

  • Artificial intelligence fundamentals :Spin-offs,High-level field,State of the art,Reasoning
  • Search: Specialized symbolic search,Constraint-based reasoning, Simple adversarial search
  • Neural networks: Perceptrons ,Feed forward networks, ,Boltzmann machines, ,autoencoders ,Backpropagation,Deep networks/deep learning,Knowledge-based reasoning,First-order logic and theorem proving
  • Rules and rule-based reasoning,Blackboard systems Structured knowledge: Frames, Conceptual Dependency,Description logic,Reasoning with uncertainty,Probability & certainty factors ,Bayesian networks ,Perception,Symbolic,Sensor processing
  • Natural language processing :Neural,Convolutional networks,Recurrent networks,Long short-term memory (LSTM) networks
  • Machine learning: Deep learning,Symbolic approaches, Multiagent systems, Societal/ethical concerns,Ensuring proper behavior, avoidance of hacking Job displacement & societal disruption,Ethics of deadly AIs: Danger of displacement of humanity,Human language technologies
  • Lexical semantics: corpora, thesauri, gazetteers., Distributional Semantics: Word embeddings, Character embeddings., Deep Learning for natural language, Applications: Entity recognition, Entity linking, classification, summarization., Opinion mining, Sentiment Analysis.
  • Language inference:Dialogic interfaces., Statistical Machine Translation.
  • NLP libraries: NLTK, Theano, Tensorflow Intelligent Systems for Pattern Recognition,Signal processing and time-series analysis,Image processing, filters and visual feature detectors
  • Bayesian learning and deep learning for machine vision and signal processing,Neural network models for pattern recognition on non-vectorial data,Kernel and adaptive methods for relational data
  • Pattern recognition applications: machine vision, bio-informatics, robotics, medical imaging, etc., ML and deep learning libraries.
  • Robotics : main definitions, illustration of application domains, Mechanics and kinematics of the robot, Sensors for robotics, Robot Control,Architectures for controlling behaviour in robots
  • Robotic Navigation,Tactile Perception in humans and robots,Vision in humans and robots,Analysis of case studies of robotic systems, Project laboratory: student work in the lab with robotic systems

Few Topics are:

  • knowledge of Artificial Intelligence (AI)
  • AI and its philosophy
  • logical reasoning
  • reasoning in the presence of uncertainty
  • machine learning
  • agency and uncertainty in AI
  • philosophical problems in AI.
  • implementation of Artificial Intelligence (AI)
  • AI technique
  • AI reasoning, planning, doing, and learning.
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