Teaching
https://baratilab.github.io/24789spring21/
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The Course I'm teaching this Semester:
The Course I'm teaching this Semester:
Artificial Intelligence and Machine Learning for Engineers 24-787 (Fall 2021):
Artificial Intelligence and Machine Learning for Engineers 24-787 (Fall 2021):
Topics we covered in the Fall 2021:
Introduction to Machine Learning and Supervised Learning
Regression
Parametric/Non Parametric Learning
Discriminative and Generative Algorithms
Naive Bayes, Non-linear Classifiers
Feature Engineering
Ensemble Methods
Support Vector Machine (SVM)
Unsupervised Learning and Clustering Algorithms
Principal Component Analysis,
Neural Networks
Physics Informed Neural Networks
Evaluation Metrics
Reinforcement Learning
Topics we will cover:
Introduction to Deep Learning and its application
Neural Networks
Convolutional Neural Networks (CNN)
Training and Testing CNN
Interpretability of Deep Learning
Graph Convolutional Neural Networks (GCNN)
Recurrent Neural Networks (RNN)
Variational Autoencoders (VAE)
Deep Generative Adversarial Networks (GAN)
Deep Reinforcement Learning (DRL)
Solving Engineering problems using Deep Learning
The Courses that I taught:
The Courses that I taught:
Artificial Intelligence and Machine Learning for Engineers 24-787 (Fall 2020):
Artificial Intelligence and Machine Learning for Engineers 24-787 (Fall 2020):
Topics we covered in the Fall 2020:
Introduction to Machine Learning and Supervised Learning
Regression
Parametric/Non Parametric Learning
Discriminative and Generative Algorithms
Naive Bayes, Non-linear Classifiers
Feature Engineering
Ensemble Methods
Support Vector Machine (SVM)
Unsupervised Learning and Clustering Algorithms
Principal Component Analysis, Independent Component Analysis
Neural Networks
Evaluation Metrics
Reinforcement Learning
Deep Learning for Engineers (Spring 2020)
Deep Learning for Engineers (Spring 2020)
Topics we will cover:
Introduction to Deep Learning and its application
Neural Networks
Convolutional Neural Networks (CNN)
Training and Testing CNN
Interpretability of Deep Learning
Graph Convolutional Neural Networks (GCNN)
Recurrent Neural Networks (RNN)
Variational Autoencoders (VAE)
Deep Generative Adversarial Networks (GAN)
Deep Reinforcement Learning (DRL)
Solving Engineering problems using Deep Learning
Artificial Intelligence and Machine Learning for Engineers 24-787 (Fall 2019)
Artificial Intelligence and Machine Learning for Engineers 24-787 (Fall 2019)
Topics we covered in the Fall 2019:
Introduction to Machine Learning and Supervised Learning
Regression
Parametric/Non Parametric Learning
Discriminative and Generative Algorithms
Naive Bayes, Non-linear Classifiers
Feature Engineering
Ensemble Methods
Support Vector Machine (SVM)
Unsupervised Learning and Clustering Algorithms
Principal Component Analysis, Independent Component Analysis
Neural Networks
Graph Neural Networks
Reinforcement Learning
Artificial Intelligence and Machine Learning for Engineers 24-787 (Fall 2018)
Artificial Intelligence and Machine Learning for Engineers 24-787 (Fall 2018)
This course provides an introduction to the fundamental methods and algorithms at the core of modern machine learning. It also covers theoretical foundations as well as essential algorithms and practical techniques for supervised and unsupervised learning.
Topics (tentative):
Introduction to Machine Learning and Supervised Learning
Regression
Discriminative and Generative Algorithms
Support Vector Machine (SVM)
Naive Bayes, Non-linear Classifiers
Unsupervised Learning and Clustering Algorithms
Principal Component Analysis, Independent Component Analysis
Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Autoencoders and Deep Generative Adversarial Networks
Reinforcement Learning/ Deep Reinforcement Learning
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