Description
This course provides a broad introduction to applied machine learning models and algorithms. Topics include: machine learning concepts; handling, cleaning, and preparing data; main categories of machine learning models; theory of optimizing a machine learning model; selecting and engineering features; selecting a model and tuning hyper-parameter using cross-validation; main challenges of machine learning.
This course will introduce models and algorithms through concrete working examples with a moderate amount of theory. Python will be used as the primary programming language for this course, together with its main scientific libraries, in particular Sklearn, NumPy, Pandas, and Matplotlib.
Prerequisites: Calculus II, Linear Algebra, Discrete Mathematics, Programming Methods II (or with permission of the instructor)
Course objectives
At the end of the course, students should be able to:
1. Handle large volume of data using python scientific libraries.
2. Understand the concepts and procedures for the most common machine learning algorithms.
3. Given a particular learning task, build a machine learning model and train the model on the training dataset.
4. Tuning a machine learning model to improve its performance.
Grading Policy
Expectations: Students are expected to learn both the material covered in class and the material in the textbook and other assigned readings. Completing homework is an essential part of the learning experience.
Grades: The grading for the course will be based on:
· Participation & In-class activities: 10%
· Homework and Quizzes: 30%
· Midterm Project: 30%
· Final Project: 30%
Required Textbook:
Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems By Aurélien Géron
Publisher: O'Reilly Media
Release Date: March 2017
Pages: 576
ISBN-13: 978-1491962299
ISBN-10: 1491962291
Online version of the textbook: http://shop.oreilly.com/product/0636920052289.do
Other resources:
Python tutorial: http://learnpython.org/
Andrew Ng’s ML course on Coursera
Python codes for the textbook: https://github.com/ageron/handson-ml
Honor Code:
You are encouraged to work together on the overall design of the programs and homework. However, for specific programs and homework assignments, all work must be your own. You are responsible for knowing and following CUNY Policy on Academic Integrity (available from the Undergraduate Bulletin, Graduate Bulletin, or the Office of Academic Standards and Evaluations).
Email:
I will be communicating with you on a regular basis throughout the semester using your email address on Blackboard. You must check your email on a regular basis. There will be no acceptable excuse for missing an email announcement.
Accommodating Disabilities:
Lehman College is committed to providing access to all programs and curricula to all students. Students with disabilities who may need classroom accommodations are encouraged to register with the Office of Student Disability Services. For more info, please contact the Office of Student Disability Services, Shuster Hall, Room 238 (Phone: 718-960-8441).