Event schedule

Coffee Break

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Group Work

Morning Break

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Lunch Break

Foundations of Machine Learning -I: Machine Learning Fundamentals

Machine Learning is everywhere transforming and changing most business (google, amazon, facebook, microsoft etc) and process in different sectors ranging from healthcare, agriculture, fashion, communication etc. As the result machine learning experts with strong theoretical foundation and practical skills on machine are highly needed especially in developing worlds. This tutorial will introduce the fundamental of machine learning with emphasis on enabling participants to learn how to build remarkable machine learning algorithms capable of solving complex real-world problem.Specifically you will: understand the basics of machine learning and its applications and learn how to formulate learning problem. Yo will also explore different challenges of machine learning algorithms. and learn best practise for evaluating machine learning algorithms

Lab -1

Foundations of Machine Learning -II:Introduction to Deep Learning

Deep learning is at the core of the current revolution in artificial intelligence for different application such as language understanding, speech processing image recognition, machine translation, planning, games and self driving car. This tutorial will cover the foundations principles of deep learning from both theoretical and implementation perspective. The goal of this tutorial will be to enable participants learn how to build remarkable deep learning algorithms capable of solving complex real-world problem. Specifically participants will understand the basic building block of deep learning model, modern deep learning architectures and their application.They will also learn how to train deep learning algorithms with emphasis on techniques used in practise to train these algorithms. Further participants will learn how to implement machine learning and deep learning algorithms using [Pytorch](http://pytorch.org/) framework

Lab-2

Deep learning for Computer Vision and Sequence Models

This tutorial will focus on deep learning techniques applicable to images and sequence data like speech, music, language etc. It will introduce the basic concepts of Convolution Neural Networks (CNN) including recent variants such as ResNet, Recurrent Neural Networks (RNNs), and its variants GRUs and LSTMs and how to build and train these models using Pytorch framework. Participants will learn how to apply these models to a typical problem such as visual detection and recognition tasks and natural language problems.

Lab-3

Unsupervised Machine Learning

In Unsupervised learning participants will learn about Clustering and dimensionality reduction: k-means clustering, hierarchical clustering,principal component analysis (PCA), singular value decomposition (SVD).They will be able to take a dataset and find the underlying structure of it, summarize and group it in most useful way and finally effectively represent data in a compressed format.

Lab -4

Machine Learning for Health: Opportunities for Data Scientists in Bioinformatics

The existence of a substantial amount of digitized biological data presents opportunities for data scientists in the field of bioinformatics. The main parts of Bionfiormatics with opportunities for upcoming data scientists are Computational biology and Software development for life science research and development. It is important to provide awareness to Tanzanian data scientists of the opportunities in bioinformatics. The basics of bioinformatics research for sequence alignment will be presented using Python

Lab-5

Machine learning in Production

The existence of a substantial amount of digitized biological data presents opportunities for data scientists in the field of bioinformatics. The main parts of Bionfiormatics with opportunities for upcoming data scientists are Computational biology and Software development for life science research and development. It is important to provide awareness to Tanzanian data scientists of the opportunities in bioinformatics. The basics of bioinformatics research for sequence comparison will be presented using Python.

ML in Production Lab

Machine Learning Research in Developing Countries

Closing Remarks

Basics of Python for Machine learning and Data Science Research

Python is a powerful, flexible, open source and widely-used programming language with many applications. This three hours lab session aim to review the basic concepts of python programming and how to use the language, with several readily-available open-source libraries for machine learning and data science. Participants will learn the basics of python and how to use it to perform numerical computing, analyse data, create beautiful visualizations, and implement machine learning algorithms.

Machine Learning Research in Developing Countries

Closing Remarks