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Description: In this course we give a broad and accessible introduction to machine learning, and review some key applications. The approach is meant to make this exciting and highly useful theory accessible to all students who are interested in learning the main mathematical ideas behind various techniques and algorithms used in machine learning. We will also have an eye towards the philosophical underpinnings of machine learning to be found in inductive reasoning.
Prerequisite: Curiosity and diligence.
Learning Outcomes:
- the ability to understand the basic principles of machine learning,
- the ability to discuss the historical development of the field,
- the ability to understand the differences between inductive and deductive reasoning,
- the ability to clearly identify what a given problem is asking for and what data are provided that might lead towards a solution using ML methods,
- the ability to plan and execute a solution strategy based on a chosen model.
Topics covered:
- Probability
- Probability densities
- Pattern recognition problem
- Optimal Bayes decision rule
- Learning from examples
- Nearest neighbor rule
- Kernel rules
- Neural Networks
- PAC learning
- VC dimension
Required Textbooks:
An Elementary Introduction to Statistical Learning Theory, S. Kulkarni and G. Harman, Wiley, 2011.
Grading:
- Participation: 10%
- Homework assignments: 20%
- Midterm: 30%
- Each homework will consist of the following parts:
- Regular problems: A set of problems chosen from several sources including the textbooks above.
- Reading assignment from the textbook or other handouts.
- Solutions must be written LEGIBLY.
- It is encouraged to discuss the problem sets with others, but everyone needs to turn in a unique personal write-up.
- A list of projects will be posted on Canvas site for the course, at the end of this page.
- Final project will be due on the last day of class.
Ground rules:
- Late homework will NOT be accepted.
- The final grade will be calculated according to the evaluation scheme given above and these grades will then be curved to determine your letter grades. However if you get less that 25/100 on the final project or your total grade is less than 45/100 your final grade will automatically be an F.
- NO extra work, extra credit or anything outside the regular homeworks and projects will be assigned. Please plan your study strategy during the term accordingly.
- Grading mistakes:
If during the semester you feel there has been a mistake made in your grading by the AIs, please contact them first. If after meeting with the AIs you still feel there is a problem with the marking, please contact me.
- Collaborative work:
One of the best ways to learn new material is to collaborate in groups. You may discuss the homework problems with your classmates, and in this way make the learning process more enjoyable. However, the homework you hand in must be your own work, in your own words and your own explanation.
- Here is the link to The Code of Student Conduct.
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