Machine Learning

This course is an introduction to machine learning concepts, techniques, and algorithms. Topics include regression analysis, statistical and probabilistic methods, parametric and non-parametric methods, classification, clustering, and neural networks. This course covers both foundational and practical aspects by (1) discussing the motivations behind popular machine learning algorithms; (2) developing a mathematical foundation for the methods of machine learning; (3) writing programs in Python for implementing machine learning algorithms; and (4) evaluating the designed systems to determine whether they work well for a particular task.

Course Structure

• Part 1: Fundamentals of Machine Learning

  • Review of Python’s main scientific libraries: NumPy, Pandas, and Matplotlib
  • Steps in a typical machine learning project
  • Learning by fitting a model to data and optimizing a cost function
  • Linear regression, regularization, logistic regression, support vector machines, decision trees/random forests, ensemble learning, K-means clustering, DBSCAN
  • Challenges of using machine learning systems

• Part 2: Neural Networks and Deep Learning

  • What neural nets are and what they are good for
  • Building and training feedforward neural nets or multilayer perceptrons (MLPs)
  • Understanding different forms of gradient descent (e.g., Nesterov, RMSProp, and Adam)
  • Convolutional neural networks (CNNs) for deep computer vision and recurrent neural nets (RNNs)

Slides

• Lecture 1: Introduction to Machine Learning, Basics of Python, Review of Linear Algebra and NumPy PDF

• Lecture 2: Review of Linear Algebra and Python Libraries PDF

• Lecture 3: Linear Regression (Gradient, Polynomial Regression, and Regularization) PDF

• Lecture 4: Logistic Regression and Classification PDF

• Lecture 5: SVMs, Decision Trees, and Random Forests PDF

• Lecture 6: Unsupervised Learning and Clustering PDF

• Lecture 7: Neural Networks and Keras PDF

• Lecture 8: Training Deep Neural Networks (Regularization and Optimization) PDF

• Lecture 9: Introduction to Convolutional Neural Networks PDF