Fall 2023
DEEP LEARNING FOR COMPUTER VISION
Recent advances in Deep Learning have propelled Computer Vision forward. Applications such as image recognition and search, unconstrained face recognition, synthetic image generation from text, object detection, semantic segmentation, and image and video captioning which only recently seemed decades off, are now being realized and deployed at scale almost everywhere. This course will look at the advances in computer vision and machine learning that have made this possible. In particular we will look at Convolutional Neural Nets (CNNs), Recurrent Neural Nets (RNNs), Transformers, Vision Transformers, and Large Language Models and their application to computer vision. We will also look at the datasets needed to feed these data hungry approaches--both how to create them and how to leverage them to address a wider range of applications.
Recent advances in Deep Learning have propelled Computer Vision forward. Applications such as image recognition and search, unconstrained face recognition, synthetic image generation from text, object detection, semantic segmentation, and image and video captioning which only recently seemed decades off, are now being realized and deployed at scale almost everywhere. This course will look at the advances in computer vision and machine learning that have made this possible. In particular we will look at Convolutional Neural Nets (CNNs), Recurrent Neural Nets (RNNs), Transformers, Vision Transformers, and Large Language Models and their application to computer vision. We will also look at the datasets needed to feed these data hungry approaches--both how to create them and how to leverage them to address a wider range of applications.
Spring 2022
ADVANCED TOPICS IN DEEP LEARNING
This is a seminar course in which the students read, present, and discuss research papers on deep learning. The focus will be mostly on applications in computer vision, but topics in natural language processing, language translation, and speech recognition will also be read and discussed. It is expected that students taking the course will have some prior experience with deep learning and neural network architectures such as Convolutional Neural Nets (CNNs), Recurrent Neural Networks (RNNs), Transformer Networks.
This is a seminar course in which the students read, present, and discuss research papers on deep learning. The focus will be mostly on applications in computer vision, but topics in natural language processing, language translation, and speech recognition will also be read and discussed. It is expected that students taking the course will have some prior experience with deep learning and neural network architectures such as Convolutional Neural Nets (CNNs), Recurrent Neural Networks (RNNs), Transformer Networks.
Fall 2021
DEEP LEARNING FOR COMPUTER VISION
Recent advances in Deep Learning have propelled Computer Vision forward. Applications such as image recognition and search, unconstrained face recognition, and image and video captioning which only recently seemed decades off, are now being realized and deployed at scale. This course will look at the advances in computer vision and machine learning that have made this possible. In particular we will look at Convolutional Neural Nets (CNNs), Recurrent Neural Nets (RNNs), Long Short Term Memories (LSTMs) and their application to computer vision. We will also look at the datasets needed to feed these data hungry approaches--both how to create them and how to leverage them to address a wider range of applications.
Recent advances in Deep Learning have propelled Computer Vision forward. Applications such as image recognition and search, unconstrained face recognition, and image and video captioning which only recently seemed decades off, are now being realized and deployed at scale. This course will look at the advances in computer vision and machine learning that have made this possible. In particular we will look at Convolutional Neural Nets (CNNs), Recurrent Neural Nets (RNNs), Long Short Term Memories (LSTMs) and their application to computer vision. We will also look at the datasets needed to feed these data hungry approaches--both how to create them and how to leverage them to address a wider range of applications.
Spring 2020
ADVANCED TOPICS IN DEEP LEARNING
This is a seminar course in which the students read, present, and discuss research papers on deep learning. The focus will be mostly on applications in computer vision, but topics in natural language processing, language translation, and speech recognition will also be read and discussed. It is expected that students taking the course will have prior experience with deep learning and neural network architectures such as Convolutional Neural Nets (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memories (LSTMs), Gated Recurrent Units (GRUs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), etc..
This is a seminar course in which the students read, present, and discuss research papers on deep learning. The focus will be mostly on applications in computer vision, but topics in natural language processing, language translation, and speech recognition will also be read and discussed. It is expected that students taking the course will have prior experience with deep learning and neural network architectures such as Convolutional Neural Nets (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memories (LSTMs), Gated Recurrent Units (GRUs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), etc..
Fall 2019
DEEP LEARNING FOR COMPUTER VISION
Recent advances in Deep Learning have propelled Computer Vision forward. Applications such as image recognition and search, unconstrained face recognition, and image and video captioning which only recently seemed decades off, are now being realized and deployed at scale. This course will look at the advances in computer vision and machine learning that have made this possible. In particular we will look at Convolutional Neural Nets (CNNs), Recurrent Neural Nets (RNNs), Long Short Term Memories (LSTMs) and their application to computer vision. We will also look at the datasets needed to feed these data hungry approaches--both how to create them and how to leverage them to address a wider range of applications.
Recent advances in Deep Learning have propelled Computer Vision forward. Applications such as image recognition and search, unconstrained face recognition, and image and video captioning which only recently seemed decades off, are now being realized and deployed at scale. This course will look at the advances in computer vision and machine learning that have made this possible. In particular we will look at Convolutional Neural Nets (CNNs), Recurrent Neural Nets (RNNs), Long Short Term Memories (LSTMs) and their application to computer vision. We will also look at the datasets needed to feed these data hungry approaches--both how to create them and how to leverage them to address a wider range of applications.
Spring 2018
DEEP LEARNING FOR COMPUTER VISION
Recent advances in Deep Learning have propelled Computer Vision forward. Applications such as image recognition and search, unconstrained face recognition, and image and video captioning which only recently seemed decades off, are now being realized and deployed at scale. This course will look at the advances in computer vision and machine learning that have made this possible. In particular we will look at Convolutional Neural Nets (CNNs), Recurrent Neural Nets (RNNs), Long Short Term Memories (LSTMs) and their application to computer vision. We will also look at the datasets needed to feed these data hungry approaches--both how to create them and how to leverage them to address a wider range of applications.
Recent advances in Deep Learning have propelled Computer Vision forward. Applications such as image recognition and search, unconstrained face recognition, and image and video captioning which only recently seemed decades off, are now being realized and deployed at scale. This course will look at the advances in computer vision and machine learning that have made this possible. In particular we will look at Convolutional Neural Nets (CNNs), Recurrent Neural Nets (RNNs), Long Short Term Memories (LSTMs) and their application to computer vision. We will also look at the datasets needed to feed these data hungry approaches--both how to create them and how to leverage them to address a wider range of applications.
Fall 2017
ADVANCED TOPICS IN DEEP LEARNING
This is a seminar course in which the students read, present, and discuss research papers on deep learning. The focus will be mostly on applications in computer vision, but topics in natural language processing, language translation, and speech recognition will also be read and discussed. It is expected that students taking the course will have prior experience with deep learning and neural network architectures such as Convolutional Neural Nets (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memories (LSTMs), Gated Recurrent Units (GRUs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), etc..
This is a seminar course in which the students read, present, and discuss research papers on deep learning. The focus will be mostly on applications in computer vision, but topics in natural language processing, language translation, and speech recognition will also be read and discussed. It is expected that students taking the course will have prior experience with deep learning and neural network architectures such as Convolutional Neural Nets (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memories (LSTMs), Gated Recurrent Units (GRUs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), etc..
Spring 2017
DEEP LEARNING FOR COMPUTER VISION
Recent advances in Deep Learning have propelled Computer Vision forward. Applications such as image recognition and search, unconstrained face recognition, and image and video captioning which only recently seemed decades off, are now being realized and deployed at scale. This course will look at the advances in computer vision and machine learning that have made this possible. In particular we will look at Convolutional Neural Nets (CNNs), Recurrent Neural Nets (RNNs), Long Short Term Memories (LSTMs) and their application to computer vision. We will also look at the datasets needed to feed these data hungry approaches--both how to create them and how to leverage them to address a wider range of applications.
Recent advances in Deep Learning have propelled Computer Vision forward. Applications such as image recognition and search, unconstrained face recognition, and image and video captioning which only recently seemed decades off, are now being realized and deployed at scale. This course will look at the advances in computer vision and machine learning that have made this possible. In particular we will look at Convolutional Neural Nets (CNNs), Recurrent Neural Nets (RNNs), Long Short Term Memories (LSTMs) and their application to computer vision. We will also look at the datasets needed to feed these data hungry approaches--both how to create them and how to leverage them to address a wider range of applications.
Spring 2014
COMPUTER VISION AND MACHINE LEARNING ON MOBILE PLATFORMS
Fall 2013
BIOMETRICS
The earliest known use of biometrics dates back to the 7th century during China's Tang Dynasty; during this period fingerprints were used to sign and validate contracts. Over the last century, biometrics -- the science for determining a person's identity by measuring his/her physiological characteristics -- has grown enormously. Technologies are being developed to verify or identify individuals based on measurements of the face, hand geometry, iris, retina, finger, ear, voice, speech, signature, lip motion, skin reflectance, DNA, and even body odor. In this course we will explore the latest advances in biometrics as well as the machine learning techniques behind them. Students will learn how these technologies work and how they are sometimes defeated. Grading will be based on homework assignments and a final project. There will be no midterm or final exam. Prerequisites: a background at the sophomore level in computer science, engineering, or like discipline.
The earliest known use of biometrics dates back to the 7th century during China's Tang Dynasty; during this period fingerprints were used to sign and validate contracts. Over the last century, biometrics -- the science for determining a person's identity by measuring his/her physiological characteristics -- has grown enormously. Technologies are being developed to verify or identify individuals based on measurements of the face, hand geometry, iris, retina, finger, ear, voice, speech, signature, lip motion, skin reflectance, DNA, and even body odor. In this course we will explore the latest advances in biometrics as well as the machine learning techniques behind them. Students will learn how these technologies work and how they are sometimes defeated. Grading will be based on homework assignments and a final project. There will be no midterm or final exam. Prerequisites: a background at the sophomore level in computer science, engineering, or like discipline.
Spring 2013
COMPUTER VISION AND MACHINE LEARNING ON MOBILE PLATFORMS