Uniandes – Summer 2018
The use of statistical models in computer algorithms allows computers to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Deep learning is the interdisciplinary field at the intersection of statistics and computer science which develops such algorithnms and interweaves them with computer systems. It underpins many modern technologies, such as speech recognition, internet search, bioinformatics, computer vision, Amazon’s recommender system, Google’s driverless car and the most recent imaging systems for cancer diagnosis are all based on Deep Learning technology.
This course on Deep Learning will explain how to build systems that learn and adapt using real-world applications. Some of the topics to be covered include deep learning frameworks, convolutional neural networks, generative models nadrecurrent models. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems, in particular, image analysis, image captioning, natural language pocessing, sentiment detection, among others.
Instructors:
– Dr. Alejandro Correa Bahnsen http://albahnsen.com
– Prof. Dr. Fabio Gonzalez http://dis.unal.edu.co/~fgonza/
Graduate assistant:
– Sergio Angulo
Course github repository: https://github.com/albahnsen/AppliedDeepLearningClass
Schedule
Introduction to Machine Learning and Neural Networks
| Date | Session | Notebook | Exercises |
|---|---|---|---|
| June-6 | Introduction to python and ML | 1 – Intro to ML 2 – Intro to Python for data analysis 3 – Linear Regression 4 – Logistic Regression | E1 – Python&Pandas E2 – Regression |
| June-8 | Machine learning systems | 5 – Data preparation and model evaluation 6 – Sampling 7 – Ensemble methods 8 – Model deployment | E3 – Cross Validation E4 – Sampling and Ensembles |
| June-20 | Neural networks basics | 9 – Intro NN 10 – Perceptron 11 – Keras | E5 – Neural Networks |
Introduction to Deep Learning
| Date | Session | Notebook | Exercises |
|---|---|---|---|
| June-22 | Introduction to deep learning and applications | 12 – Intro to Deep Learning | |
| July-4 | Deep learning frameworks | 13 – Intro to Tensorflow 14 – Deep learning frameworks | E6 – TensorFlow and Keras |
Deep Learning for Image Analysis
| Date | Session | Notebook | Exercises |
|---|---|---|---|
| July-5 | Deep learning for image analysis & CNN | 15 – CNN training in Keras | P2 – Image classification with CNN |
| July-6 | CNN and transfer learning | 16 – Convolutional neural networks 17 – Transfer learning | E7 – Image Captioning |
| July-11 | Generative models | 18 – Generative adversarial networks |
Deep Learning for Text Analysis
| Date | Session | Notebook | Exercises |
|---|---|---|---|
| July-12 | Intro to NLP and Intro to RNN | 19 – Intro to natural language processing | E8 – Sentiment classification |
| July-13 | Word2vec & RNN for text analysis | 20 – Recurrent neural network and LSTM 21 – Word3Vec | E9 – Sequence classification using LSTM |
Final Project
| Date | Session | Readme | Kaggle |
|---|---|---|---|
| July-23 | Final project presentations | Description | Link Kaggle |