INFO-I 418 Deep Learning Neural Networks
- Prerequisites: MATH 171 Multidimensional Mathematics, a Python programming course (e.g., CSCI 23000, CIT 21500, 24200, or 27000, INFO-I 223 or INFO-I 210), and a statistics course (e.g., ECON E270, PBHL B300, 301, or 302, PSY B305, SPEA K300, STAT 30100, or STAT 35000)
- Delivery: On-Campus
Deep learning has resurged with the availability of massive datasets and affordable computing, enabling new applications in computer vision and natural language processing. This course introduces convolutional, recurrent, and other neural network architectures for deep learning. Students design, implement, and train these models to solve real-world problems.
- Solve problems in linear algebra, probability, optimization, and machine learning.
- Evaluate, in the context of a case study, the advantages and disadvantages of deep learning neural network architectures and other approaches.
- Implement deep learning models in Python using the PyTorch and TensorFlow libraries and train them with real-world datasets.
- Design convolution networks for handwriting and object classification from images or videos.
- Design recurrent neural networks with attention mechanisms for natural language classification, generation, and translation.
- Evaluate the performance of different deep learning models (e.g., with respect to the bias-variance trade-off, overfitting and underfitting, estimation of test error).
- Perform regularization, training optimization, and hyperparameter selection on deep models.
- Analyze a deep learning model’s hardware node and GPU scalability in preparation for deployment.
There is not a syllabus available for this course.