Teaching

Machine Learning II -  DSC 324 

Syllabus

Introduction: Biological Neuron, Idea of computational units, McCulloch–Pitts unit and Thresholding logic, Linear Perceptron, Perceptron Learning Algorithm, Linear separability. Convergence theorem for Perceptron Learning Algorithm. 

Feedforward Networks: Multilayer Perceptron, Gradient Descent, Backpropagation, Empirical Risk Minimization, regularization.

Deep Neural Networks: Difficulty of training deep neural networks, Greedy layerwise training. 

Better Training of Neural Networks: Newer optimization methods for neural networks (Adagrad, adadelta, rmsprop, adam, NAG), second order methods for training, Saddle point problem in neural networks, Regularization methods (dropout, drop connect, batch normalization). 

Convolutional Neural Networks: Architectures, convolution/pooling layers, LeNet, AlexNet.

Recurrent Neural Networks: Backpropagation through time, Long Short Term Memory, Gated Recurrent Units, Bidirectional LSTMs, Bidirectional RNNs. 

Generative models: Restrictive Boltzmann Machines (RBMs), Introduction to MCMC and Gibbs Sampling, gradient computations in RBMs, Deep Boltzmann Machines.

Deep Unsupervised Learning and Recent Trends: Autoencoders (standard, sparse, denoising, contractive, etc), VariationalAutoencoders, Adversarial Generative Adversarial Networks, Autoencoder and DBM, Multi-task Deep Learning, Multi-view Deep Learning. 

Applications of Deep Learning to Computer Vision: Image segmentation, object detection, automatic image captioning, Image generation with Generative adversarial networks, video-to-text with LSTM models. Attention models for computer vision tasks. 

 Applications of Deep Learning to NLP: Introduction to NLP and Vector Space Model of Semantics Word Vector Representations: Continuous Skip-Gram Model, Continuous Bag-of-Words model (CBOW), Glove, Evaluations and Applications in word similarity, analogy reasoning.

Text Books

1. Ian Goodfellow and YoshuaBengio and Aaron Courville, Deep Learning, MIT Press, 2016. 

2. Bishop, C. ,M., Pattern Recognition and Machine Learning, Springer, 2006. 

3. Raúl Rojas, Neural Networks: A Systematic Introduction, Springer, 1996. 

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 Data Analysis & Visualization - DSC 415

Syllabus

Introduction to Data Analysis & Visualization: 

Data Analytics, Role of Data Analytics, Components of Data Analytics, Types of Data Analytics, Data Analytics Techniques, Advantages of Data Analytics, Applications of Data Analytics, Data Analytics Tools. Data Visualization, Importance of Data Visualization, Categories of Data Visualization, Types of Data Visualization Techniques, Advantages and Disadvantages of Data Visualization, Difference Between Data Visualization and Data Analytics, Data Visualization Techniques, Data Visualization Tools, Benefits of Data Visualization Tools, Common features of Data Visualization Tool, Data Visualization Libraries, Data Visualization examples, Concerns Regarding Data Visualization.

Data Analysis and Visualization Using Python: 

Steps from data to knowledge, Data analysis and processing, Python libraries in data analysis, How to choose the right Python libraries Data Visualization (Line Plot, Bar Plot, Pie Chart, Box Plot, Histogram Plot, Scatter Plot):- Direct Plotting, Seaborn Plotting System, Matplotlib Plot Case Studies:- Data Gathering, Data Analysis, Data Visualization, Findings Advanced Visualization:- Simulation, Animation Machine Learning Models with scikit-learn:- Logistic Regression, Random Forest, Naive Bayes Classifier, Support Vector Machine, Gradient Boosting, Decision Tree,  KNN.

Visualization for deep learning: 

An Interactive overview of model analysis, Why do we want to visualize models?, What can we visualize? When is visualization most relevant? How can we visualize models? Tools and frameworks for model visualization Understanding, Visualizations, and Explanation of Deep Neural Networks, Deep Learning Visualization Methods, Visualizing a Neural Network using Keras Library, Visualize Deep Learning Models using Visualkeras, Why Model Visualization is Important? What are the benefits of Model Visualization?

Visualization Toolkit (VTK) : 

Introduction to Visualization Toolkit (VTK) for 3D computer graphics, Image processing and visualization, Visualization Pipeline, Isosurfaces, Volume rendering, Vector field visualization, And biological and medical data applications.

Data Visualization BI Tool: 

Introduction to Data Visualization with Tableau, Exploring Data Visualization with Tableau, What is Data Visualization, Exporting Data and Working with Tableau

Building Data Visualization BI Project With Tableau: 

BI Reporting Understanding, Report and Dashboard Template Document, Tableau Design and Development Database Source Connection 

Text Books

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