Day 3 (7/10/19): Experimentation with Classifiers

Today I took a step forward toward a more specific focus on machine learning and neural networks. After quickly reviewing yesterday's material through Datacamp exercises on python and Numpy, I spent most of the morning completing tutorials on implementing various classifiers using the machine learning software Scikit-learn.

First using the simple K Nearest Neighbors Classifier, I learned how to fit classifiers to data, use the classifiers to predict targets for new test data, and evaluate the accuracy and errors of each classifier using Confusion Matrices and Classification Reports. Then, I applied what I learned to both the Iris (classifying three types of flowers) and MNIST (classifying handwritten digits) datasets implementing different types of models such as Support Vector Machine (both Linear and RBF), Gaussian Process, and Multilayer Perceptron Classifiers.

In the afternoon, I watched a Youtube playlist which provided an introduction to neural networks including a basic Python implementation of Linear Regression. After finishing that simple neural network, I started learning more specifically about Convolutional Neural Networks (CNNs), especially used in computer vision problems. With guidance from resources such as Stanford's CS231 and RIT's Deep Learning for Vision courses, I hope to be able to implement CNNs in code later this week.

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Day 29 (8/15/19): Final Day Before Presentations

Most of today was also spent practicing and editing my presentation to make it as professional as I can. I'm really looking forward to the opportunity to present my work to faculty and friends tomorrow. Here is a link to the slides for my final presentation: Novelty Detection in Streaming Learning using Neural Networks

Day 28 (7/14/19): Presentation Dry Run

In the morning, all of us interns got the chance to practice our presentations in front of each other in the auditorium. I was pretty happy with how mine went overall but the experience was definitely valuable in identifying typos or slight adjustments that should be made. Throughout the rest of the day, I tried to implement these changes and clean up a few plots that I want to include for Friday.

Day 9 (7/18/19): Incrementally Learning CUB200

Today I continued my work learning about incremental learning models by testing out different strategies on the CUB200 dataset. From what I understand from reading various articles, there seem to be five different approaches to mitigating catastrophic forgetting in lifelong learning models. These are regularization methods (adding constraints to a network's weights), ensemble methods (train multiple classifiers and combine them), rehearsal methods (mix old data with data from the current session), dual-memory methods (based off the human brain, includes a fast learner and a slow learner), and sparse-coding methods (reducing the interference with previously learned representations). All of these methods have their constraints and I don't believe it is yet clear what method (or what combination of different methods) is best. Full rehearsal obviously seems to be the most effective at making the model remember what it had previously learned but given that all training exam...

RIT CIS Summer Internship

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