Pattern Recognition, Neural Networks & Deep Learning
Learning Outcomes:
- Understanding the fundamental concepts and techniques of pattern recognition and machine learning
- Analyzing datasets to extract relevant features for classification tasks
- Implementing linear and nonlinear classifiers like perceptrons and support vector machines
- Designing and training artificial neural networks for various learning tasks
- Applying dimensionality reduction techniques like PCA to preprocess high-dimensional data
- Evaluating the performance of machine learning models using appropriate metrics
- Implementing ensemble methods like bagging and boosting to improve classifier accuracy
- Utilizing deep learning architectures like convolutional neural networks for complex tasks
- Comparing different machine learning algorithms to select the most suitable for a given problem
- Interpreting the internal representations learned by deep neural networks
- Addressing common issues in machine learning like overfitting and class imbalance
- Implementing generative models like GANs to create synthetic data
- Applying transfer learning to leverage pretrained models for new tasks
Skills for module:
Python
Object Oriented Programming
Functional Programming
Machine Learning
Deep Learning
Artificial Intelligence
Data Science
Neural Networks
Computer Vision
Scikit Learn
Pandas
NumPy
Matplotlib
Seaborn
Keras
TensorFlow
Jupyter Notebooks
Probability
Statistics
Calculus
Linear Algebra
Algorithms
Data Structures
Problem Solving
Critical Thinking
Time Management
Continuous Integration
Data Visualisation
Mathematics
Reinforcement Learning
Pattern Recognition, Neural Networks & Deep Learning
7CCSMPNN
Learning Outcomes
- Understanding the fundamental concepts and techniques of pattern recognition and machine learning
- Analyzing datasets to extract relevant features for classification tasks
- Implementing linear and nonlinear classifiers like perceptrons and support vector machines
- Designing and training artificial neural networks for various learning tasks
- Applying dimensionality reduction techniques like PCA to preprocess high-dimensional data
- Evaluating the performance of machine learning models using appropriate metrics
- Implementing ensemble methods like bagging and boosting to improve classifier accuracy
- Utilizing deep learning architectures like convolutional neural networks for complex tasks
- Comparing different machine learning algorithms to select the most suitable for a given problem
- Interpreting the internal representations learned by deep neural networks
- Addressing common issues in machine learning like overfitting and class imbalance
- Implementing generative models like GANs to create synthetic data
- Applying transfer learning to leverage pretrained models for new tasks
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