MachineLearning_Course_Spring2023

Team

• Guillermo Arredondo, student of a double BS degree in Data Science and Applied Mathematics at ITAM.
• Iñaki Fernandez, student of a BS degree in Data Science at ITAM.
• Mauricio Vazquez, student of a double BS degree in Data Science and Actuarial Science at ITAM.

Repository Content

• Naive Bayes Algorithm Implementation
  • Naive Bayes Notebook
  • Naive Bayes Summary
• Linear Discriminant Analysis (LDA)
  • LDA Notebook
  • LDA Summary
• Perceptron
  • Perceptron Notebook
  • Perceptron Summary
• Gradient Descent
  • Gradient Descent Notebook
  • Gradient Descent Summary
• Linear Regression
  • Linear Regression Notebook
  • Lineas Regression Summary
• Support Vector Machine (SVM)
  • Kernels Notebook
  • SVM Summary
• Neural Network
  • Neural Network Notebook
  • Neural Network Summary
• Random Forest
  • Random Forest Notebook
  • Random Forest Summary
• Clustering
  • Optics Notebook
  • Optics Summary
• Reinforcement Learning
  • Centipede Reinforcement Learning Notebook
  • Reinforcement Learning Summary

Course objective

"Gain an in-depth understanding of some of the major machine learning techniques: its algorithms, theory and application. In the same way, that he becomes familiar, through practice, with the procedure of elaboration of a model."

-Salvador Marmol, Machine Learning course professor

Course Syllabus

• Machine Learning concepts
• Supervised learning
  • Basic Bayes method
  • K-Nearest neighbors
  • Linear regression
  • Neural network
  • Support vector machine
  • Decision tree
• Models evaluation and learning theory
• Unsupervised learning
  • A-priori algorithm
  • K-means clustering
  • Hierarchical clustering
  • Density-Based clustering
  • Dimensionality reduction method
    • PCA
    • T-SNE
• Recomendation system
• Model Assemblies
  • Random forest
  • Bagging
  • Boosting
• Deep Learning
  • Convolutional neural network
• Reinforcement Learning
  • Deep reinforcement learning

Bibliography

• Murphy, K. (2022) Probabilistic Machine Learning: An Introduction. Cambridge, MA: MIT.
• Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer Series in Statistics, 2nd edition.
• Bishop, C. M. (2006) Pattern Recognition and Machine Learning, New York, N. Y.: Springer Science + Business Media.
• Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. (2017) Deep Learning. Cambridge, MA: MIT.
• Sutton, Richard S., and Andrew G. Barto. (2018) Reinforcement Learning: An Introduction. Cambridge, MA: MIT, 2nd edition.