# Difference between revisions of "Course:CPSC522"

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*[[Course:CPSC522/User-Adaptive Information Visualization|User-Adaptive Information Visualization]] | *[[Course:CPSC522/User-Adaptive Information Visualization|User-Adaptive Information Visualization]] | ||

==Existing Combinations (2018)== | ==Existing Combinations (2018)== | ||

+ | ===Neural Networks=== | ||

*[[Course:CPSC522/Financial Forecasting using LSTM Networks |Financial Forecasting using LSTM Networks]] (2018) | *[[Course:CPSC522/Financial Forecasting using LSTM Networks |Financial Forecasting using LSTM Networks]] (2018) | ||

+ | * [[Course:CPSC522/Character Level Language Models using LSTM|Character Level Language Models using LSTM]] (2018) | ||

*[[Course:CPSC522/TextSummarizationUsingMachineLearning |Text Summarization using Machine Learning]] (2018) | *[[Course:CPSC522/TextSummarizationUsingMachineLearning |Text Summarization using Machine Learning]] (2018) | ||

+ | * [[Course:CPSC522/Password_cracking_using_PCFGs_and_Neural_Networks|Password Cracking using Probabilistic Context Free Grammars and Neural Networks]] (2018) | ||

+ | * [[CNNs in Image Segmentation]](2018) | ||

+ | * [[Course:CPSC522/Image_Classification_With_Convolutional_Neural_Networks|Image Classification With Convolutional Neural Networks]] (2018) | ||

+ | * [[Image Colonization using Deep Learning ]](2018) | ||

*[[Course:CPSC522/StackedGAN|Stacked Generative Adversarial Networks]] (2018) | *[[Course:CPSC522/StackedGAN|Stacked Generative Adversarial Networks]] (2018) | ||

− | + | ===Reinforcement Learning=== | |

− | + | ||

* [[Course:CPSC522/Deep_Q-Learning|Deep Q-Learning]] (2018) | * [[Course:CPSC522/Deep_Q-Learning|Deep Q-Learning]] (2018) | ||

− | |||

− | |||

* [[Course:CPSC522/Deep_Reinforcement_Learning|Deep Reinforcement Learning]] (2018) | * [[Course:CPSC522/Deep_Reinforcement_Learning|Deep Reinforcement Learning]] (2018) | ||

+ | * [[Course:CPSC522/Self_Improving_Machines|Self-Improving Machines]] (2018) | ||

+ | * [[Course:CPSC522/Adaptive_Network_Routing_using_ACO|Adaptive Network Routing using Ant Colony Optimization]] (2018) | ||

+ | ===Decision-theoretic Planning=== | ||

* [[Course:CPSC522/Action_Selection_for_MDPs|Action Selection for MDPs]] (2018) | * [[Course:CPSC522/Action_Selection_for_MDPs|Action Selection for MDPs]] (2018) | ||

+ | * [[Course:CPSC522/Rao_Blackwellized_Particle_Filtering|Rao-Blackwellized Particle Filtering]](2018) | ||

+ | ===Relational Reasoning=== | ||

* [[Course:CPSC522/Transfer_Learning_with_Markov_Logic|Transfer Learning with Markov Logic]] (2018) | * [[Course:CPSC522/Transfer_Learning_with_Markov_Logic|Transfer Learning with Markov Logic]] (2018) | ||

+ | ===Applications=== | ||

* [[Course:CPSC522/Affect Prediction using Eye Gaze|Affect Prediction using Eye Gaze]] (2018) | * [[Course:CPSC522/Affect Prediction using Eye Gaze|Affect Prediction using Eye Gaze]] (2018) | ||

* [[Course:CPSC522/Cognitive_Robotics|Cognitive Robotics]] (2018) | * [[Course:CPSC522/Cognitive_Robotics|Cognitive Robotics]] (2018) | ||

− | |||

− | |||

− | |||

* [[Course:CPSC522/Conflict-Driven Clause Learning for the Boolean Satisfiability Problem|Conflict-Driven Clause Learning for the Boolean Satisfiability Problem]] (2018) | * [[Course:CPSC522/Conflict-Driven Clause Learning for the Boolean Satisfiability Problem|Conflict-Driven Clause Learning for the Boolean Satisfiability Problem]] (2018) | ||

− | |||

==Future combinations (2016)== | ==Future combinations (2016)== |

## Revision as of 20:02, 8 March 2018

CPSC 522 Wiki | |
---|---|

CPSC 522 | |

Section: | |

Instructor: | David Poole |

Email: | poole@cs.ubc.ca |

Office: | 109 |

Office Hours: | after class every day |

Class Schedule: | TH 2:00-3:30 |

Classroom: | DMP 101 |

Important Course Pages | |

Syllabus | |

Lecture Notes | |

Assignments | |

Course Discussion | |

Welcome to CPSC 522 Wiki. This is where the participants are writing the textbook. See http://www.cs.ubc.ca/~poole/cs522/2018/ for the main web page for the course.

## Contents

## Some Rules

- 2018 Student Presentation Schedule is the schedule for presentations.
**Please sign up early. This is first-come first-choice.**There is a maximum of 3 students per day. - January Assignment describes your assignment for January.
- February Assignment describes your assignment for February.
- March-April Assignment describes your 3rd assignment.
- Do not plagiarize; give references to all sources. Put quotes in quotes.
- All pages should follow the CPSC522 Template
- Give links, but make sure that the pages are readable (with sentences) without following the links.
- Link to all prerequisite pages (those the page builds on) in the appropriate place in the template. The prerequisite pages should not form a cycle.
- Link to all related pages; it should be easy for someone to determine that a page on a particular topic does not exist.
- The web pages created should all be in the
**Course:CPSC522**hierarchy, but should not contain "Course:CPSC522" in the visible link. Hint: create a link to the page before the page exists, then you will be asked to create the page, - If you want to abandon a page that others are relying on (e.g., you are the principal author and they are coauthors) you need to negotiate with them to make sure no one is disadvantaged.
- All pages should be in the Index and the table of contents below.

## Guidelines

- Keep each page as simple as possible (but not simpler); if a page starts to get complicated, consider splitting it.
- Write pages for your peers; they should all be written for incoming graduate students, and only assume background knowledge that is common among such students.
- All pages should obey the Syntax Conventions. If there is a design decision that you need to make that may have non-local implications, add it to the conventions.
- It should use formalism and mathematics when (and only when) the formalism make the description clearer. Use the code tags for math, e.g., It is worth your while to learn Latex if you don't already know it.
- If there is a simple case, and a more general case, give the simple case first. Making things complicated is easy; keeping them simple is difficult and we should strive for simplicity. Any complication needs to be carefully motivated.
- Use the "discussion" tab

Each Page should contain:

- A clear jargon-free description of what is going on. Keep jargon to a minimum.
- Motivating example(s) and, where appropriate, a simple pedagogical example (which may be different from the motivating examples) that is used to explain what is going on
- An argument of plausibility
- Evidence that it works
- Code and pseudo-code, where appropriate. This code should interact with other related code (e.g., AIFCA Python Distribution) if possible. The code should be as simple as possible to implement the techniques. Consider adding exercises as to what can be improved or made more general or bullet-proof. Use a
`code block`

for (pseudo-)code (even multi-line code). You can also use the format in http://wiki.ubc.ca/Course:CPSC_320/Midterm_2_Reference_Sheet#Pseudocode (try both and see which better suits your needs).

## Table Of Contents

Please add your page here and in the Index.

### General

- J0: Artificial General Intelligence (2018)
- J1: Swarm Intelligence (2018)

### Control

- Control Theory (2016)
- Hierarchical Control (2016)

### Probability and Graphical Models

- Probability (2016)
- Graphical Models (2016)
- Bayesian Networks (2016)
- Markov Networks (2016)
- Hidden Markov Models (2016)
- Markov Chain Monte Carlo (2016)
- Particle Filtering (2016)
- Variable Elimination (2016)
- Causality (2016)
- Neural Network (2016)
- J2: Markov Chains (2018)
- J3: Recurrent Neural Networks (2018)
- J4: Kalman filter (2018)
- J5: Decision Trees (2018)
- J6: Learning Probabilistic Models with Complete Data (2018)
- J7: Support Vector Machines (2018)
- J8: Ensemble Learning (2018)
- J9: Natural Language Processing (2018)
- J10: Probabilistic Context Free Grammars (2018)
- J11: Dynamic Bayesian Networks (2018)

### Utility and Preferences

- J12: Bounded Rationality (2018)

### Acting Under Uncertainty

- Decision Networks (2016)
- Markov Decision Process (2016)
- J13: Multi-Agent Systems (2018)
- Reinforcement Learning (2016)
- Reinforcement Learning with Function Approximation (2016)
- Game Theory (2016)
- J14: Stochastic Optimization (2018)

### Logic

- J15: Abduction (2018)
- Knowledge Compilation (2016)
- Predicate Calculus (2016)
- J16: Markov Logic (2018)
- J17: Higher Order Logic (2018)

## Existing Combinations (2016)

- Recommendation System using Matrix Factorization
- Latent Dirichlet Allocation
- Deep Neural Network and Game of Go
- Problog
- Maximum Entropy Markov Models
- Ontology Search Engine
- Convolutional Neural Networks
- Learning Markov Logic Network Structure
- Decision Support System using Interactive Preference Elicitation
- Predicting Affect of User's Interaction with an Intelligent Tutoring System
- Record Linkage and identity uncertainty
- Robot Scientist
- Density-Based Unsupervised Learning
- Predicting Human Behavior in Normal-Form Games
- Identity Uncertainty
- Generative Adversarial Networks
- Inductive Logic Programming
- User-Adaptive Information Visualization

## Existing Combinations (2018)

### Neural Networks

- Financial Forecasting using LSTM Networks (2018)
- Character Level Language Models using LSTM (2018)
- Text Summarization using Machine Learning (2018)
- Password Cracking using Probabilistic Context Free Grammars and Neural Networks (2018)
- CNNs in Image Segmentation(2018)
- Image Classification With Convolutional Neural Networks (2018)
- Image Colonization using Deep Learning (2018)
- Stacked Generative Adversarial Networks (2018)

### Reinforcement Learning

- Deep Q-Learning (2018)
- Deep Reinforcement Learning (2018)
- Self-Improving Machines (2018)
- Adaptive Network Routing using Ant Colony Optimization (2018)

### Decision-theoretic Planning

### Relational Reasoning

### Applications

- Affect Prediction using Eye Gaze (2018)
- Cognitive Robotics (2018)
- Conflict-Driven Clause Learning for the Boolean Satisfiability Problem (2018)

## Future combinations (2016)

- Sentiment Analysis: Movie Reviews
- Collaborative Filtering
- List Recommendation
- Inactive Cookie Mapping via Trail Matching
- Improve Recommendation System by Integration
- Identity Uncertainty in a restaurant data-set
- Regularization for Neural Networks
- Spam Detection
- Titanic: Machine Learning from Disaster
- Automatic Classification of Morphological Heart Arrhythmia
- Linking Sentences in Asynchronous Conversations
- Generic Aspect-based Aggregation of Sentiments
- Graph Based Key-corporation Extraction
- Improving the accuracy of Affect Prediction in an Intelligent Tutoring System
- Improving Human Behavior Prediction in Simultaneous-Move Games
- The Automation of Disease Diagnosis
- Analyzing online dating trends with Weka
- Artificial Intelligence and Economic Theory

## Subpages

This is automatically generated. If someone can make it work, please do! I tried using what is at Help:Using_the_UBC_Wiki_for_Course_Work...

Or categories:

- CNNs in Image Segmentation
- CPSC522/AGI
- CPSC522/Abduction
- CPSC522/Action Selection for MDPs
- CPSC522/Adaptive Network Routing using ACO
- CPSC522/Affect Prediction using Eye Gaze
- CPSC522/Artificial Intelligence and Economic Theory
- CPSC522/Artificial Neural Network
- CPSC522/Automatic Classification of Morphological Heart Arrhythmia
- CPSC522/Bayesian Networks
- CPSC522/Bounded Rationality
- CPSC522/Causality
- CPSC522/Character Level Language Models using LSTM
- CPSC522/Cognitive Robotics
- CPSC522/Collaborative Filtering
- CPSC522/Conflict-Driven Clause Learning for the Boolean Satisfiability Problem
- CPSC522/Convolutional Neural Networks
- CPSC522/Decision Networks
- CPSC522/Decision Support System using Interactive Preference Elicitation
- CPSC522/Decision Trees
- CPSC522/Deep Neural Network
- CPSC522/Deep Q-Learning
- CPSC522/Deep Reinforcement Learning
- CPSC522/Density-Based Unsupervised Learning
- CPSC522/Disgust
- CPSC522/Dynamic Bayesian Networks
- CPSC522/Ensemble Learning
- CPSC522/Financial Forecasting using LSTM Networks
- CPSC522/Future Directions for Semantic Systems
- CPSC522/Game Theory
- CPSC522/Generative Adversarial Networks
- CPSC522/Generic Aspect-based Aggregation of Sentiments
- CPSC522/Graph Based keyword extraction
- CPSC522/Graphical Models
- CPSC522/Handwriting Digits Recognition
- CPSC522/Hidden Markov Models
- CPSC522/Hierarchical Control
- CPSC522/Higher Order Logic
- CPSC522/Identity Uncertainty
- CPSC522/Identity Uncertainty in a restaurant data-set
- CPSC522/Image Classification With Convolutional Neural Networks
- CPSC522/Image Recognition With Deep Neural Networks
- CPSC522/Improving Human Behavior Prediction in Simultaneous-Move Games
- CPSC522/Improving the accuracy of Affect Prediction in an Intelligent Tutoring System
- CPSC522/Inactive Cookie Mapping via Trail Matching
- CPSC522/Interactive Preference Elicitation
- CPSC522/Kalman filter
- CPSC522/Knowledge Compilation
- CPSC522/LSTMs
- CPSC522/Latent Dirichlet Allocation
- CPSC522/Learning Markov Logic Network Structure
- CPSC522/Learning Probabilistic Models with Complete Data
- CPSC522/List Recommendation
- CPSC522/MCMC
- CPSC522/Markov Chains
- CPSC522/Markov Decision Process
- CPSC522/Markov Logic
- CPSC522/Markov Networks
- CPSC522/Maximum Entropy Markov Models
- CPSC522/Multi-Agent Systems
- CPSC522/Natural Language Processing
- CPSC522/Ontologies
- CPSC522/PCFG
- CPSC522/Password cracking using PCFGs and Neural Networks
- CPSC522/Predicate Calculus
- CPSC522/Predicting Affect of User's Interaction with an Intelligent Tutoring System
- CPSC522/Predicting Human Behavior in Normal-Form Games
- CPSC522/Prediction of the Annual Income of Household Based on Demographics Attributes
- CPSC522/Probability
- CPSC522/Probability general semantics
- CPSC522/Problog
- CPSC522/Rao Blackwellized Particle Filtering
- CPSC522/Record Linkage and identity uncertainty
- CPSC522/Recurrent Neural Networks
- CPSC522/Regularization for Neural Networks
- CPSC522/Reinforcement Learning
- CPSC522/Reinforcement Learning with Function approximation
- CPSC522/Self-Referential Machines
- CPSC522/Self Improving Machines
- CPSC522/Self Referential Machines
- CPSC522/StackedGAN
- CPSC522/Stochastic Optimization
- CPSC522/Support Vector Machines
- CPSC522/Swarm Intelligence
- CPSC522/Template
- CPSC522/Temporal Difference
- CPSC522/TextSummarizationUsingMachineLearning
- CPSC522/The Automation of Disease Diagnosis
- CPSC522/Transfer Learning with Markov Logic
- CPSC522/User-Adaptive Information Visualization
- CPSC522/Utility
- CPSC522/Variable Elimination
- Image Colonization using Deep Learning
- Image Colourization using Deep Learning

## Suggested Unclaimed Pages

Here are some possible topics for pages. This list is not meant to limit your imagination. Some of them might be better split into multiple pages. There are many other possible topics.

When claimed, these pages should be moved from this section to the table of contents above and to the Index of existing pages. To claim a page you have to actually create it and edit it (and have your name on the page, so everyone can see who has claimed it).

- Probability - general semantics with infinitely many variables and/or variables with infinite domains
- Representations of Conditional Distributions
- Partially Observable Markov Decision Processes
- Recursive Conditioning
- Weighted Model Counting
- Variational Inference
- Parity Methods for Probabilistic Inference
- Matrix Factorization
- Utility
- Multi-Attribute Utility
- Value of Information
- Preference Elicitation
- Mechanism Design
- Logic Programming
- Negation as Failure
- Equality/Identity
- Inductive Logic Programming
- Ontologies