Faculty Development Program
ONE-WEEK FACULTY DEVELOPMENT PROGRAMME (FDP-2019)
"Machine Learning & Deep Learning Techniques"
(17th – 22nd June, 2019)
Organized by (Venue)
School of Computer Engineering and Technology
MIT Academy of Engineering, Pune
In Association with
National Social Summit (NSS) IIT Roorkee
ABOUT FDP TRAINING
The objective of the FDP is to introduce fundamentals of data mining and machine learning techniques with their real-time applications.
Overall, this FDP is serving to be a great platform for faculty and researchers to upgrade their knowledge in the area of ML Deep learning, computer vision and its applications.
FDP on Machine Learning, Deep Learning techniques are concerned with Data the analysis, design and development of methods for the classification or description of patterns, objects, signals and processes.
OBJECTIVES OF THE FDP
- To provide exposure to fundamentals of Artificial intelligence, Deep Learning, Data Mining and Machine Learning
- To enable the participant to define a research problem and develop a suitable methodology for addressing the problem.
- To make participants understand, learn and use various analysis methods (tools) in AI research.
- Image identification and Classification using Machine Learning
- Google stock price prediction using Deep Learning
- Modern Face Recognition with Deep Learning
- Develop a predictive analytics model for a complex data set
- Deep Learning for recognizing handwritten digits (MNIST dataset)
- Simple Spam-Detecting Deep Learning Classifier
- Case Study: Character Recognition
- Case Study: Iris Clustering,
COURSE OUTLINES FOR ML & DEEP LEARNING
- Machine Learning Basics, Basics of Deep learning,
- Designing & Optimizing Deep Neural Network Model
DATA MINING & PROCESSING
- Content: Distribution of data set, Data Augmentation Modern Deep Networks,
- Deep Feedforward Networks, Regularization, Optimization for Training Deep Models.
CONVOLUTIONAL NEURAL NETWORK
- Content: Introduction to CNN's, Properties of CNN representations: invertibility, stability, Covariance/invariance: capsules and related model, Principles behind CNNs, Sequence Modelling
RECURRENT NEURAL NETWORK
- Content: Recurrent Neural network, Bidirectional RNNs, Basics of Recursive neural network and Long Short-Term Memory Network (LSTM), RNN applications.
- Learn & Interact with renowned Industry Experts.
- One-week Certificate of Participation by Eduxlabs in Association with NSS IIT Roorkee
- Project-based learning on Machine Learning & Deep Learning.
Team EduxLabs (Esoir Business Solution )
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