Machine learning is concerned with the question of how to construct computer programs that learn from experience. Machine learning systems have been developed and used in a wide range of settings, ranging from spam detection, to medical diagonosis, to recommendation systems that adapts to and even defines our preferences, to autonomous vehicles that learn to drive. There have also been important advances in the theory and algorithms that form the foundation of this field. This course will provide a broad introduction to the field of machine learning.
By the end of this course, you’ll be able to take a problem and analyze it to determine which machine learning techniques are appropriate for solving the problem, how to prepare data to use that solution, apply the solution, and to evaluate the results. For the most common machine learning techniques, you’ll also be able to implement solutions in Python.
It is required that you have regular access to a computer and an Internet connection throughout this course. A laptop is preferable.
We will have two lectures every week. We will have midterm exam in class. We place strong emphasis on the final project which allows students to develop skills in multiple aspects.
Mathematical background: Linear algebra, probability, calculus, and statistics. We have a quiz in the first lecture. This does not contribute to your final scores, but it helps both yourself and the instructors to know your mathematical background.
Programming: We will make extensive use of the Python programming language. Most homeworks will be in python3.
We do not allow for any late submissions. Note that since we are only considering the best four homework scores, it is totally fine if you miss one homework.
Meeting ID: 335-163-142
Here is how you will be able to get connected:
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