Welcome to Machine learning course. What is machine learning? It is concerned with the question of how to write computer programs that automatically improve with experience. Over the very recent years, the field has expanded so much in every direction of our daily lives that we mostly are unaware of its existence. But, as a concerned citizen of the world, we are going to know the nuts and bolts of machine learning.

The course is based on fundamental knowledge of computer science principles and techniques, probability and statistics, calculus, and the theory and application of linear algebra. The course provides a broad introduction to pattern recognition from given data and how it can relate to machine learning. Topics include supervised learning, unsupervised learning, semi-supervised learning, neural network, and reinforcement learning algorithms. The course will also discuss recent applications of these machine learning concepts in solving real-world problems.

Course objectives

By the end of the course you are expected to gain the following skills:

1. Develop an understanding on how to extract patterns from data.
2. Develop an understanding on a wide variety of machine learning algorithms – how the algorithms work, and their practical usages.
3. Understand different types of optimization techniques, which are heavily utilized in many of the learning algorithms.
4. Capable of discussing pros and cons of the learning algorithms.
5. Be able to implement the covered algorithms in class by themselves using Python programming language.
6. Apply the algorithms to solve real world problems.

Prerequisites

For undergraduates:

1. MATH-3195 (Linear algebra and differential equations)
2. CSCI-3412 (Algorithms).

For graduate students:

1. The graduate standing.

Topics covered

1. Introduction to Machine Learning
2. Story of Learning
3. Nuts and bolts of Machine Learning
4. Linear model: Linear regression
5. Classification with k-NN
6. Clustering with k-means
7. Curse of dimensionality (and PCA)
8. Other regression models
9. Hierarchical clustering algorithms
10. Logistic Regression
11. Binary to multi-class classification
12. Ethics and Bias in AI
13. Naive Bayes Classification
14. Ensemble Learning
15. AdaBoost
16. Classification with the Support Vector Machines (SVMs)
17. Artificial Neural Nets and Deep Learning
18. Reinforcement Learnning