Data Science Initiative for Africa (DSI-A) Training Programme

A Short Course in Deep Learning with a Focus on Health Data Science

All lectures will start from 9:00 AM - 4:30 PM for both Virtual BootCamp AND In-person.

Virtual BootCamp: 19 – 23 June 2023

Please use the following link: ZOOM LINK.

In-person Deep Learning Course: 26 June – 7th July 2023

Venue: Elangeni Hotel, 63 Snell Parade, North Beach, Durban, 4001.

Program Timetables

Below is an outline of the current plan for course topics and schedule. It is intended to serve as guidance, the specific topics and schedule may change at the decision of the course instructional staff.

Virtual BootCamp

Date	Type	Topic	Facilitator(s)
Monday, 19 June 2023	Lecture	Introduction to epidemiology/global health and health data science	Dr. Palwasha Khan & Dr. Stephen Olivier
Tuesday, 20 June 2023	Lecture	Introduction to epidemiology/global health and health data science	Dr. Palwasha Khan & Dr. Stephen Olivier
Wednesday, 21 June 2023	Lecture	Introduction to Python and Jupyter Notebooks	Dr. Mandlenkosi Gwetu & Dr. Kennedy Chengeta
Thursday, 22 June 2023	Lecture	Introduction to Python and Jupyter Notebooks	Dr. Mandlenkosi Gwetu & Dr. Kennedy Chengeta
Friday, 23 June 2023	Lecture	Introduction to Python and Jupyter Notebooks	Dr. Mandlenkosi Gwetu & Dr. Kennedy Chengeta

In-person Lectures

Week 1
Time	Monday 26/06	Tuesday 27/06	Wednesday 28/06	Thursday 29/06	Friday 30/06	Saturday 01/07
Facilitator(s)	Santiago, Sandra	Santiago, Sandra, Mandla	Santiago, Sandra, Mohanad	Santiago, Sandra, Mohanad	Santiago, Sandra, Mohanad	Santiago, Sandra, Mohanad
9:00-10:30	Introduction to Deep Learning, Deep Learning vs Machine Learning	Introduction to Backpropagation and Multilayer Perceptrons (MLPs)	MLPs in Python with Keras	Introduction to Convolutional Neural Networks (CNNs), Convolution layers, pooling layers, fully connected layers	CNNs Basics, Fine Tuning and Visualzing the CNNs Model	Group Discussion & Mini Project (in Global Health and Climate Change)
10:30-11:00	Break	Break	Break	Break	Break	Break
11:00-12:30	Unsupoervised Machine Learning	Introduction to MLPs Activiation functions, Regularization Techniques	Regularization	Transfer Learning and Fine-Tuning, Pre-trained Models, Data Agumentation using pretrained networks	Training and Testing Models on Samples Datasets	Group Discussion & Mini Project (in Global Health and Climate Change)
12:30-13:30	Lunch	Lunch	Lunch	Lunch	Lunch	Lunch
13:30-14:30	Neural Networks Architecture Basics	Lab Session	Lab Session	Lab Session	Lab Session	Group Presentations
14:30-14:45	Break	Break	Break	Break	Break	Break
14:45-16:00	Simple Examples of Deep Learning in Global Health Research	Lab Session	Lab Session	Lab Session	Lab Session	Group Presentations
16:00-16:30	Office Hours	Office Hours	Office Hours	Office Hours	Office Hours	Office Hours

Week 2
Time	Monday 03/07	Tuesday 04/07	Wednesday 05/07	Thursday 06/07	Friday 07/07
Facilitator(s)	Santiago, Sandra, Mandla, Kennedy	Santiago, Sandra, Mandla, Kennedy	Santiago, Sandra, Mandla, Kennedy	Santiago, Sandra, Mohanad, Gabriel	Santiago, Sandra, Mohanad, Gabriel
9:00-10:30	Introduction to Recurrent Neural Networks (RNNs), Overview of different types of RNNs (vanilla RNNs, LSTM, GRUs)	RNNs Basics, Gradient flow and backpropagation Through Time (BPTT)	RNNs Basics Continued (LSTM), LSTM and GRU cells	Introduction to Transformers and Transfer Learning	Project Competition
10:30-11:00	Break	Break	Break	Break	Break
11:00-12:30	Basic Structure of RNNs and How they work, Application of RNNs in Global Health Research	RNNs Basics, Gradient flow and backpropagation Through Time (BPTT)	Implementing a Simple RNN in Python (Tenserflow)	Pre-Trained models, Fine-Tuning	Project Competition
12:30-13:30	Lunch	Lunch	Lunch	Lunch	Lunch
13:30-14:30	Lab Session	Lab Session	Lab Session	Lab Session	Course Evaluation
14:30-14:45	Break	Break	Break	Break	Break
14:45-16:00	Lab Session	Lab Session	Lab Session	Lab Session	Closing Ceremony
16:00-16:30	Office Hours	Office Hours	Office Hours	Office Hours	Closing Ceremony