This is the webpage for “The foundation of applied machine learning” for Spring 2019 by Prof. Bahram Mobasher. You can find the related materials (homeworks, codes, jupyter notebooks, …) under the link. You can get/clone the repository of this course from my github page as well.

What is this course about?

Machine Learning is about building automated methods that improve their own performance through learning patterns in data, and then use the uncovered patterns, which acts as our theoretical model, to predict the future and make decisions. Examples include document, image, and handwriting classification, spam filtering, face/speech recognition, decision making, automated navigation to name a few. This course covers the theory and practical algorithms for machine learning from a variety of perspectives.

Video lectures for online students:

Videos are accessible in the iLearn under section(002)

Syllabus of the course:

Syllabus

Time table for the rest of the quarter

Courses:

• Week 1: Python 3

• Week 2: Statistics

• Week 3: More Statistics + Monte Carlo Simulation

• Week 4: More Statistics + Confidence interval

• Week 5: Machine Learning: Clustering and Classification

• Week 6: Machine Learning: Regression, Gradient Descent, Decision Trees

• Week 7: Machine Learning: Decision Trees, Random Forests, Support Vector Machine

Lectures:

Lecture 1 Lecture 2 Lecture 3 Lecture 4 Lecture 5 Lecture 6

Lecture 7 Lecture 8 Lecture 9 Lecture 10 Lecture 11 Lecture 12

Lecture 13 Lecture 14 Lecture 15 Lecture 16 Lecture 17 Lecture 18

Assignments:

• Week 1

• Week 2

• Week 3

• Midterm projects and datasets zip file

• Week 4

• Week 5 and datasets zip file

• Final project and dataset txt file

Useful hacks and cheat sheets:

• Linear Algebra
• Numpy
• matplotlib
• Jupyter notebook