Instructor: Alejandro Correa Bahnsen
- email: [email protected]
- twitter: @albahnsen
- github: albahnsen
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. Machine learning is the interdisciplinary field at the intersection of statistics and computer science which develops such algorithms 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 Machine Learning technology.
This course on Time Series Analysis, Machine Learning and Natural Language Processing will explain how to build systems that learn and adapt using real-world applications. Some of the topics to be covered include time series analysis, machine learning, python data analysis, natural language processing models and recurrent models. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems, in particular, churn modeling, natural language processing, sentiment detection, among others.
- Python version 3.7;
- Numpy, the core numerical extensions for linear algebra and multidimensional arrays;
- Scipy, additional libraries for scientific programming;
- Matplotlib, excellent plotting and graphing libraries;
- IPython, with the additional libraries required for the notebook interface.
- Pandas, Python version of R dataframe
- Seaborn, used mainly for plot styling
- scikit-learn, Machine learning library!
A good, easy to install option that supports Mac, Windows, and Linux, and that has all of these packages (and much more) is the Anaconda.
GIT!! Unfortunatelly out of the scope of this class, but please take a look at these tutorials
- 75% Projects (3 projects, 25% each)
- 15% Exercises
- 10% Class participation
| Date | Session | Notebooks/Presentations | Exercises |
|---|---|---|---|
| June 16th | ARIMA Processes | ||
| June 18th | Working with TSA |
| Date | Session | Notebooks/Presentations | Exercises |
|---|---|---|---|
| June 23rd | Decision Trees & Ensembles | ||
| June 24th | Random Forest and XGBoost | ||
| June 25th | Machine Learning as a Service |
| Date | Session | Notebooks/Presentations | Exercises |
|---|---|---|---|
| June 30th | Natural Language Processing | ||
| July 1st | Sentiment Analysis | ||
| July 2nd | NLP using Neural Networks | 16 - LSTM |