Machine Learning Workshop in Nepal

Kist College in association with National ICT Council and ICT Frame Magazine is organizing a Nepal’s workshop on Machine Learning and Data Science 2018 in following Date and Time.

Machine Learning Workshop in Nepal

Event Details: May 9-11, 2018, Kathmandu
Venue: KIST College, Kamalpokhari, Kathmandu
Registration Fee:- Rs 1500

Facilitator’s Background
Jhanak Parajuli works as a data scientist and machine learning researcher in msg systems ag. Germany, which is a consulting, solutions and partnership multinational company working in different sectors such as- insurance, banking, automotive, public sector, health, food, etc.

Tej Bahadur Shahi works as Asst. Prof. of Computer Science at Central Department of Computer Science and IT, Tribhuvan University. Mr. Shahi has more than four years of experience in Nepal government as IT officers and seven years of experiences in teaching computer sciences.

Ashok Kumar Pant works as Senior Machine Learning Software Engineer at Treeleaf Pvt. Ltd. Mr. Pant has more than seven years of experience in machine learning and software development fields.

Workshop Information Overview
Workshop Title National Workshop on Machine Learning and Data Science, 2018

Workshop Description The primary goal of our workshop is to provide basic knowledge on different machine learning and deep learning algorithms along with the hands-on experiences on a complete data science pipeline with possible dataset using python.
The intended audiences are undergraduate or graduate students from computer science, Information Technology, Information management, Staffs or faculties with interests in data science and machine learning or company employees with similar interests.

Handouts, Materials, Supplies Lecture slides and other related materials

Detailed Workshop Plan
Schedule Day 1 
11:00 AM – 1:00 PM Basics of Machine Learning 
• Python installation and basic knowledge of python packages such as numpy, pandas, and sci-kit-learn
• Revision of Basic Linear Algebra and Calculus
Review of Probability and Statistics
• About Kaggle competition
• The big picture of machine learning and applications
• Different approaches to machine learning

1:00 PM – 2:00PM – Lunch Break

2:00PM – 4:00 PM – Supervised Machine Learning
• The concept of bias, variance, and regularization
• The idea of train, test, and validation
• Different metrics for machine learning
• Supervised machine learning algorithms: Regression and Classification
• Linear Regression using python /R
• Polynomial Regression
• Logistic Regression

Day 2: 
11:00 AM – 1:00PM –Supervised Machine Learning (contd..)
• Different metrics for machine learning- Accuracy, confusion matrix, F1 Score, etc.
• Naive Bayes
• Decision Trees and Random Forests
• Support Vector Machines
• Gradient Boosting (introduction)

1:00 PM – 2:00PM – Lunch Break

2:00PM – 4:00 PM – Unsupervised Machine Learning
• K Nearest Neighbors
• K Means Clustering
• Dimensionality Reduction (Principal Component Analysis)
• Introduction to Neural network

Day 3:
11:00 AM – 1:00PM –Neural Networks
• The concept of Artificial Neural Network
• Backpropagation
• Examples
• Introduction to deep learning

1:00 PM – 2:00PM – Lunch Break

2:00PM – 4:00 PM – Deep Learning
• Applications of deep learning
• Introduction to Recurrent Neural Network with text
• Introduction to Convolutional Neural Network with image
• Concluding Remarks

Resource Persons:- 
Dr. Jhanak Parajuli, Mr. Tej Bahadur Shahi, Mr. Ashok Kumar Pant


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