Electronics & ICT Academy (Under Ministry of Electronics and Information Technology (MeitY), Govt. of India)

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1 Electronics & ICT Academy (Under Ministry of Electronics and Information Technology (MeitY), Govt. of India) Indian Institute of Technology Guwahati, Guwahati, Assam, Pin Phone: /3009, Faculty Development Programme on Deep Learning & Machine Learning Venue: ECE Dept, Gauhati University (19 24 March, 2018) 6-DAY SCHEDULE DAY TOPIC DURATION SPEAKER DAY 1 (19/03/18) DAY 2 (20/03/18) Registration & Reporting Inauguration Introduction: Problem framing Feature selection Bayes decision theory for pattern recognition Supervised and unsupervised classifications Parametric and nonparametric schemes. Practical session on Machine Learning Unsupervised learning: Clustering Vector Quantization EM Algorithm Dimensionality reduction using PCA and other methods Discriminative classifiers: LDA Multi-layer perceptron Back propagation SVM AM AM AM AM AM AM AM PM PM PM PM PM PM PM PM PM PM PM AM AM AM AM AM AM Dr. Manas Kamal Bhuyan, Associate Professor, Dept. of Electronics & Electrical Engineering, IIT Guwahati

2 DAY 2 (20/03/18) DAY 3 (21/03/18) Applications of neural networks and fuzzy logic in pattern recognition Practical session on Machine Learning Decision Trees Random Forest Introduction to Artificial Intelligence Introduction to Artificial Intelligence Applications, Industries, and growth Techniques used for AI AI for everything Different methods used for AI Tradition Methods & New Methods AI Agents Introduction of Machine Leaning Introduction of Machine Leaning Application of Machine Learning Machine Learning Introduction Supervised & Unsupervised Learning Regression & Classification Problems Semi-Supervised & Reinforcement Learning Linear Regression Regression Problem Analysis Mathematical modelling of Regression Model Gradient Descent Algorithm Programming Process Flow Use cases AM PM PM PM PM PM PM PM PM PM PM PM 9.30 AM AM Dr. Manas Kamal Bhuyan, Associate Professor, Dept. of Electronics & Electrical Engineering, IIT Guwahati

3 Logistic Regression Problem Analysis Cost Function Formation Mathematical Modelling Use Cases 2.00 PM 3.30 PM DAY 3 (21/03/18) Artificial Neural Networks Neurons, ANN & Working Single Layer Perceptron Model Multilayer Neural Network Feed Forward Neural Network Cost Function Formation Applying Gradient Descent Algorithm Backpropagation Algorithm & Mathematical Modelling 3.30 PM 3.45 PM 3.45 PM 5.00 PM DAY 3 (21/03/18) 5.00 PM 5.15 PM

4 (22/03/18) Mathematical Computing with Python (NumPy) NumPy Overview Properties, Purpose, and Types of ndarray Class and Attributes of ndarray Object Basic Operations: Concept and Examples Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays Copy and Views Shape Manipulation Linear Algebra Data Manipulation with Python (Pandas) Introduction to Pandas Data Structures Series DataFrame Missing Values Data Operations Data Standardization 9.30 AM 9.50 AM (22/03/18) Pandas File Read and Write Support Data Acquisition (Import & Export) Selection, Filtering, Combining and Merging Data Frames, Normalization method Data Visualization in Python using Matplotlib Introduction to Data Visualization Python Libraries Plots Matplotlib Features: Line Properties Plot with (x, y) Controlling Line Patterns and Colors 9.50 AM AM AM AM

5 (22/03/18) Set Axis, Labels, and Legend Properties Multiple Plots Subplots, Seaborn Implementation of Linear Regression Programming Using python Building simple Univariate Linear Regression Model Multivariate Regression Model Boston Housing Prizes Prediction Cancer Detection Predictive Analysis Best Fit Line and Linear Regression Implementation of Logistic Regression Digit Recognition using Logistic Regression Programming Flow for backpropagation algorithm Use Cases of ANN Programming SLNN using Python Programming MLNN using Python Digit Recognition using MLNN Diabetes Data Predictive Analysis using ANN 2.00 PM 3.30 PM 3.30 PM 3.45 AM 3.45 PM 5.15 PM (22/03/18) 5.15 PM 5.30 PM

6 DAY 5 DAY 5 Clustering Hierarchical Clustering K Means Clustering Use Cases for K Means Clustering Programming for K Means using Python Image Color Quantization using K Means Clustering Technique Principle Component Analysis Dimensionality Reduction, Data Compression Concept and Mathematical modelling Use Cases Programming using Python IRIS Data Analysis using PCA Lunch Deep Dive into Neural Networks Understand limitations of A Single Perceptron Understand Neural Networks in Detail Backpropagation Learning Algorithm Understand Backpropagation Using Neural Network Example 9.30 AM AM and 2.00 PM 3.00 PM 3.00 PM 3.15 PM 3.15 PM 5.00 PM DAY PM 5.15 PM

7 DAY 6 Convolutional Neural Networks (CNN) Introduction to CNNs CNNs Application Architecture of a CNN Convolution and Pooling layers in a CNN Understanding and Visualizing a CNN Transfer Learning and Fine-tuning Convolutional Neural Networks Image classification using Keras deep learning library Lunch Recurrent Neural Networks (RNN) Intro to RNN Model Application use cases of RNN Modelling sequences Training RNNs with Backpropagation Long Short-Term memory (LSTM) Recursive Neural Tensor Network Theory Recurrent Neural Network Model Time-Series Analysis Closing Ceremony 9.30 AM AM And 3.00 PM 3.15 PM 2.00 PM 3.00 PM And 3.15 PM 5.00 PM 5.00 PM 5.15 PM PM PM Signature of Technical Assistant E&ICT, IIT Guwahati