Lecture Time: MWF 11:00a11:50a CENTR 212
Lab hour: Wednesday, 2:00p5:00pm, CSB 115, computer lab
TA:
Web Resources
Text Books: 1. Kevin P. Murphy, "Machine Learning: a Probabilistic Perspective", 2013. here
2. R. Duda, P. Hart, D. Stork, "Pattern Classification", second edition, 2000.
This course is selfcontained; having the textbook is helpful but not absolutely necessary. Office Hours: Piazza
Please enroll in this webpage to receive class notification.
This course is one part of a twocourse foundation that forms a rigorous introduction to machine learning. COGS 118A and COGS 118B are independent courses that may be taken in either order. Introduction to machine learning (I) will prepare the students in basics of the statistical classification methods which will likely serve the foundation for data analysis and inference in a variety of applications. It will also be helpful in learning more advanced statistical machine learning algorithms, which have been applied in a wide range of scenarios for studying and predicting cognitive models, financial models, social behaviors, brain growth patterns, and visual inference. You will need to use either Matlab or Python to do your assignments and final project. You can pick either one (Matlab or Python) or get consent from the instructor if you would pick a programming language of your choice.
Prerequisites: Mathematics 20F (Linear Algebra) or Mathematics 31AH (Honors Linear Algebra), and Mathematics 180A (Introduction to Probability) or ECE 109 (Engineering Probability & Statistics), and COGS 109 (Modeling and Data Analysis) or CSE 11 (Introduction to Computer Science & ObjectOriented Programming: Java ), or consent of instructor. Grading policy: Assignments: 35%
Midterms: 40%
Final project: 20%
Classroom participation: 5% Bonus points: 5%
Late policy: every 5% of the total points will be deducted for every extra day past due for the homework assignments and the final project.
