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This two-week intensive winter program will equip participants with deep theoretical foundations and expose them to the latest developments and cutting-edge research being conducted in the area of Data Science. There will be presentations/lectures on advanced background material not typically covered in undergraduate mathematics and statistics courses.
This two-week intensive winter program will equip participants with deep theoretical foundations and expose them to the latest developments and cutting-edge research being conducted in the area of Data Science. There will be presentations/lectures on advanced background material not typically covered in undergraduate mathematics and statistics courses.
Date: 2 - 13 July 2018
Venue: AIMS South Africa, Muizenberg
Courses & Instructors:
Courses & Instructors:
- Probability & Statistics for Data Science - Terence van Zyl (Wits)
- Deep Learning - Emmanuel Darfouq (AIMS South Africa) & Bubacarr Bah (AIMS South Africa)
- Network Analytics - Franck Kalala Mutombo (AIMS AIMS Senegal & University of Lubumbashi) & Bubacarr Bah (AIMS South Africa)
Course Outlines:
Course Outlines:
Probability & Statistics for Data Science
- Exploratory Data Analysis:
Introduction to Exploratory Data Analysis and statistical distributions.
- Discrete Distributions:
Understanding probability mass functions and cumulative distribution functions.
- Modeling Distributions:
Modeling data using distributions.
- Continuous Distributions:
Exploring probability density functions.
- Multivariate Relationships:
Introduction to multivariate data and its visualization.
- Estimation and Hypothesis Testing:
Theory of estimating parameters and hypothesis testing.
- Linear Least Squares and Regression:
The use of least square estimation for linear regression.
The free online text book we will use is: http://greenteapress.com/thinkstats2/thinkstats2.pdf
Deep Learning (DL)
- DL basics
- Interesting applications
- Intro to Tensorflow
- Intro to TFLearn and PyTorch
- Numpy Refresher
- Training/theory
- Sentiment analysis
- CNNs
- Weight initialisation
- Autoencoders
- Project: Dog breed classification
- RNNs
- LSTMs
- Transfer learning
- Word2Vec and Embeddings
- Intro to reinforcement learning
- Intro to generative adversarial networks
Network Analytics
- The basic conceptual and mathematical formulation of networks
- Basic metrics of networks (e.g. paths, components, degree distributions, etc.)
- Centrality measures
- General properties of real world networks
- Models of networks
- Dynamics of, and on, networks (e.g. percolation and resilience, growth, spreading, random walks, etc.)
- Community detection
- Social Network Analysis
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