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Introduction to Machine Learning
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Class Details (^) Introduction to Machine Learning
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Prerequisites (^) Calculus, Linear Algebra, Basic Algorithms, (highly recommended: Probability and Statistics)
Class Description
Machine learning is an exciting and fast-moving field of computer science with many recent consumer applications (e.g., Microsoft Kinect, Google Translate, Iphone's Siri, digital camera face detection, Netflix recommendations, Google news) and applications within the sciences and medicine (e.g., predicting protein-protein interactions, species modeling, detecting tumors, personalized medicine). This course introduces undergraduate computer science students to the field of machine learning. Students learn about the theoretical foundations of machine learning and how to apply machine learning to solve new problems. Assuming no prior knowledge in machine learning, the course focuses on two major paradigms in machine learning which are supervised and unsupervised learning. In supervised learning, we learn various methods for classification and regression. Dimensionality reduction and clustering are discussed in the case of unsupervised learning The course will consist of lectures and lab sessions.
Desired Outcomes
The goal of this course is to familiarize undergraduate computer science students with basic principles in machine learning and prepare them for solving data-driven problems.
Assessment Components
Homework (40%) + Final Exam (30%) + Final Project (30%) Homework: bi-weekly, starting from the second week of the semester. Each homework consists of one or two questions together with one or less programming assignment. This must include number of pages of written work and time of oral presentations.
Failure to submit or fulfil any required course component results in failure of the class.
Be as specific as possible about your expectations regarding student work
Assessment Expectations
Grade A: 90+
Grade B: 70+
Grade C: 60+
Grade D: < 60
Grade F: Failure in any of three assessment components
Grade conversion A = 16 Félicitations
A- = 15 Excellent
B+ = 14 Très bien
B = 13 Bien
B- = 12 Encourageant/Assez bien
C+ = 11 Moyen plus
C = 10 Moyen
C- = 9 Passable
D+ = 8
D = 7
D- = 6
Grading Policy NYU Paris aims to have grading standards and results in all its courses similar to those
that prevail at Washington Square.
Attendance Policy
Here is NYU’s Attendance Policy for students studying away at a Global Academic Center :
Study abroad at Global Academic Centers is an academically intensive and immersive
experience, in which students from a wide range of backgrounds exchange ideas in discussion-
based seminars. Learning in such an environment depends on the active participation of all
students. And since classes typically meet once or twice a week, even a single absence can cause
a student to miss a significant portion of a course. To ensure the integrity of this academic
experience, class attendance at the centers is mandatory, and unexcused absences will
affect students' semester grades. Students are responsible for making up any work missed due
to absence. Repeated absences in a course may result in failure.
Beginning Fall 2014, at all Global Academic Centers, unexcused absences will be
penalized with a two percent deduction from the student’s final course grade 1.
Other guidelines specific to NYUParis include:
- Attendance to class and all course-related events, even outside of regularly
scheduled course times, is expected and mandatory. Some class outings/make-
up classes take place on Fridays
- Under no circumstances will non-University-related travel constitute an
excused absence from class. DO NOT book travel until you have received and
carefully studied the syllabus of each of your classes.
- If you are not sick enough to go to the doctor, you are well enough to go to class.
Doctor’s notes will be expected for all medical-related absences.
- No tests, quizzes, or exams will be made up. A missed test, quiz, or exam will
1 NYU’s “Policies and procedures for students studying away at a Global Academic Center”
Required Text(s) (^) by Kyunghyun Cho (available online at https://github.com/nyu-dl/Intro_to_ML_Lecture_Note)
Supplemental Texts(s) (not required to purchase as copies are in NYU-L Library)
Kevin Murphy. Machine Learning: a Probabilistic Perspective. 2nd edition. 978- Chris Bishop. Pattern Recognition and Machine Learning. 978-0-387-31073- Andreas C. Müller & Sarah Guido. Introduction to Machine Learning with Python. 978-
Internet Research Guidelines
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Additional Required Equipment
Students are encouraged to bring their own laptops to the lab sessions.
Session 1
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Supervised Learning
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Session 2
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Perceptron and Logistic Regression
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Session 3
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Loss Functions and Support Vector Machines
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Session 4
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Overfitting, Regularization and Model Complexity
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Session 5
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Multi-Class Classification and Weight Visualization
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Watch the video lecture <The Best Stats You've Ever Seen> by Hans Rosling: https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen
Session 6
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Nonlinear Classification: k-Nearest Neighbour Classifier and Radial Basis Function Networks
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Session 7
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Nonlinear Classification: Adaptive Basis Function Network
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Session 8
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Linear Regression, Recap of Probability and Distributions
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Session 9
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Bayesian Linear Regression
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Session 10
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Dimensionality Reduction via Matrix Factorization
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Session 11
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Principal Component Analysis and Non-Negative Matrix Factorization
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Session 12
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Nonlinear Principal Component Analysis: Deep Autoencoders
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Watch the video lecture <How we're teaching computers to understand pictures> by Fei-Fei Li: https://www.ted.com/talks/fei_fei_li_how_we_re_teaching_computers_to_understand_pictures
Session 13
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Metric Multi-Dimensional Scaling (MDS), k-Means Clustering
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Watch the interview by Geoff Hinton : https://www.youtube.com/watch?v=XG-dwZMc7Ng
Session 14
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k-Means Clustering, Course Recap
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Session 15 (^) [Click here and enter info about final exam, submission of project or individual meetings.]
