ttarsi / ix-data-science

Course material for iXperience Data Science 2018.

learning-materials

Course material for iXperience Data Science 2018. Explanatory notes and code for work in deep learning, machine learning and data science.

Course Structure

Week 1: Data Science Fundamentals

	Monday	Tuesday	Wednesday	Thursday	Friday
Topic Summary	Introduction to Data Science	Introduction to Python	Fundamentals of data Manipulation in Python	Data visualization	Collaborative work and version control
Class structure	The pipeline from data to models in production. Deep learning and the data scientist's skillset.	Syntax, data structures: lists, dictionaries, functions, classes.	Numpy and Pandas.	Matplotlib deep dive.	Git and Github.
Homework Assignments	Vim, Tmux, navigating the terminal.	Python programming exercises.	Data structures in python and view construction in pandas.	Plotting figures with Matplotlib.	Collaborative project extracting features from cryptocurrency trading and order book data.

Week 2: Introduction to Machine Learning

	Monday	Tuesday	Wednesday	Thursday	Friday
Topic Summary	Introduction to Machine Learning	Machine Learning algorithms	Evaluation of classifiers	Essential SQL for data scientists	Spark and Big Data
Class structure	Quality of fit, bias variance trade-off,decision boundaries.	Tree-based methods, support vector machines, hyperparameter optimization.	Class imbalance, ROC, precision and recall, confusion matrices, boosting.	Declarative languages, SQL syntax, selecting, grouping, joining, indices and optimisation.	RDDs, big data pipelines and the PySpark API.
Homework Assignments	Plotting decision boundaries, evaluating model complexity, bias and variance. Cross validation	Hyperparameter optimisation: grid search vs random.	Modelling with class imbalance, rigorous model evaluation.	SQL exercises.	Spark pipeline for feature extraction.

Week 3: Advanced Machine Learning

	Monday	Tuesday	Wednesday	Thursday	Friday
Topic Summary	Dimensionality reduction	Clustering	GPU Server Setup	Introduction to neural networks	Convolutional networks
Class structure	Linear vs non-linear dimensionality reduction. PCA, t-SNE.	Density-based clustering, DB-SCAN, hierarchical clustering.	GPU acceleration, Nvidia CUDA and CUDNN.	Feedforward networks motivation and development, introduction to the Keras API.	Why convolutions, genesis and building blocks of convolutional models, transfer learning.
Homework Assignments	t-SNE, density and preseved quantities.	Assessing clustering quality.	Setting up a GPU server for deep learning with Google Cloud Compute.	Feedforward networks with Keras.	Convolutional networks and transfer learning.

Week 4: Deep Learning

	Monday	Tuesday	Wednesday	Thursday	Friday
Topic Summary	Recurrent models	Recurrent models	Autoencoders	Model productionization	Putting it all together
Class structure	Simple RNN cells, memory and vanishing gradients.	Generators, LSTMs and implementation.	Foundations of autoencoders and unsupervised learning.	Model serving and APIs with Flask and Celery	Integrating model design and productionization.
Homework Assignments	Recurrent model intuitions.	Temperature prediction and generative sequence modelling.	Generative adversarial network design.	Creating a web server to host a trained model.	Start-to-finish modelling pipeline.