Machine Learning Mini-course

This repository contains the material of the mini course of ML taught by the laboratory of Computational Biology of the Universidad EAFIT.

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Schedule and Syllabus

Unless otherwise specified both lectures and discussion sections are weekday but friday 13:00 to 16:00

Event Type	Date	Description	Course Materials
Lecture 1	Monday Dec 3 33-102	Course Introduction The Machine Learning Landscape What is? Why use? Types Testing and Validation Course overview	[slides]
Discussion Section	Monday Dec 3 33-102	The Wonderful World of Python Python overview numpy / pandas matplotlib / plotly and data visualization	[slides] [python/numpy tutorial]
Lecture 2	Tuesday Dec 4 33-102	Training Models Linear regression Gradient descent Polynomial regression Learning curves Logistic regression	[slides]
Lecture 3	Tuesday Dec 4 33-102	Notable models Decision Trees Support Vector Machines Ensemble Learning Random Forests	[slides]
Discussion Section	Tuesday Dec 4 33-102	End-to-End Machine Learning Project	[notebook]
Lecture 4	Wednesday Dec 5 33-102	Unsupervised Learning Clustering Define the clustering problem Types of clustering Clustering algorithms Dimensionality Reduction The curse of dimensionality Main approaches for dimensionality reduction PCA	[slides]
Lecture 5	Thursday Dec 6 33-202	Introduction to Artificial Neural Networks Bio-inspired algorithms Biological neurons Perceptron Multi-layer perceptron Fine-Tuning	[slides]
Discussion Section	Thursday Dec 6 33-202	Tensorflow	[slides]
Lecture 6	Monday Dec 10 34-502	Training Deep Neural Net Vanishing gradient problem Initialization Nonsaturating activation function Reusing Pretrained Layers Regularization	[slides]
Lecture 7	Tuesday Dec 11 34-502	Convolutional Neural Networks Visual cortex Convolutional layer Pooling later CNN architectures	[slides]
Discussion Section	Tuesday Dec 11 34-502	Transfer learning	[notebook]
Lecture 8	Wednesday Dec 12 34-502	Recurrent Neural Networks Memory Cells IO sequences Training RNNs LSTM cell GRU cell	Soon
Discussion Section	Thursday Dec 13 33-103	Practical Deep Learning Chiron: A basecaller for Oxford Nanopore Technologies sequencers Pneumonia Detection	Soon

Note: This Syllabus will be subject to changes according to the interests of the attendees.

Instructor:

Santiago Hincapie-Potes
Email - shincapie[at]acm[dot]org
Github - https://github.com/shpotes