Data Visualization & Pattern Recognition
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Follow on Instagram for UpdatesData Visualization & Pattern Recognition Syllabus — Unit-wise Content
The official GTU curriculum for Data Visualization & Pattern Recognition (subject code ME01095061) as prescribed for AIAD · Semester 1 divides the course into 7 units, covering 46 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME01095061)Unit 1 Introduction
- How visualization affects data interpretation
- Role of visualization in data science
- Two flavors of data visualization: exploratory and communicative
Unit 2 Visualization Design Principles
- Data and task abstraction
- Best practices for encoding
- Marks and channels
- Effectiveness and expressiveness
- How to critique visualizations
- Design problems and consequences
- How not to cause misinterpretation
Unit 3 Exploratory Visualization Techniques 6 24
- Program Name: Master of Engineering Level: PG Branch: Computer Engineering (Artificial Intelligence And Data Science) Course / Subject Code : ME01095061 Subject Name: Data Visualization & Pattern Recognition Handling high-dimensional data
- Comparison techniques
- small multiples
- handling uncertainty
- Depicting time
Unit 4 Interactive Visualization
- Why interactivity is needed
- handling multiple views
- Brushing and Linking
- D3 instructions and assignments
Unit 5 Pattern Recognition system
- Machine Perception
- classification of pattern recognition systems
- Design of Pattern recognition system
- Pattern recognition Life Cycle
- Statistical Pattern Recognition: Review of probability theory
- Gaussian distribution
- Bayes decision theory and Classifiers
- Optimal solutions for minimum error and minimum risk criteria
- Normal density and discriminant functions
- Decision surfaces
- Parameter estimation methods
Unit 6 Maximum-Likelihood and Bayesian Parameter Estimation
- Maximum-Likelihood Estimation
- Bayesian Estimation
- Bayesian Parameter Estimation: General Case & General Theory
- Sufficient Statistics
- Problems of Dimensionality
- Component Analysis and Discriminants
- Expectation-Maximization (EM)
Unit 7 Multilayer Neural Networks
- Feedforward Operation and Classification
- Backpropagation Algorithm
- Error Surfaces
- Backpropagation as Feature Mapping
- Bayes Theory and Probability
- Practical Techniques for Improving Backpropagation
- Second-Order Methods
- Additional Networks and Training Methods
- Complexity Adjustment and Pruning
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