Overview: 

This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbor, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks. Feature reduction methods will also be discussed. We will introduce the basics of computational learning theory. In the course we will discuss various issues related to the application of machine learning algorithms. We will discuss hypothesis space, overfitting, bias and variance, tradeoffs between representational power and learnability, evaluation strategies and cross-validation. The course will be accompanied by hands-on problem solving with programming in Python and some tutorial sessions.

This course covers the basic concepts and techniques of Machine Learning from both theoretical and practical perspective. The material includes classical ML approaches such as Linear Regression and Decision Trees, more advanced approaches as Clustering and Association Rules as well as “hot” topics such as XGBoost. The students will be able to experiment with implementations of almost all algorithms discussed in class using meaningfully crafted Jupyter notebooks and practice quizzes.

Training Objectives:

Upon completion of this course, participants will be able to:

1. Analyze and identify significant characteristics of data sets.
2. Develop an understanding of training a learning algorithm including over-fitting, noise, convergence and stopping criteria.
3. Match a data set with the most promising inductive learning algorithms.
4. Understand and implement the training, testing, and validation phases of learning algorithms development and deployment.
5. Determine the computational complexity associated with development and execution of learning algorithms for a given data set.
6. Develop hands on experience with the leading set of inductive learning algorithms.
7. Apply machine learning algorithms for classification and functional approximation or regression.