Introduction to Machine Learning

Instructor: Arti Ramesh
Open year round
Delivery: Self-paced online, pre-recorded video lectures in addition to self-assessment quizzes (not graded) and final exam (graded)
Credentials: The students who successfully complete the course by passing the final exam will receive the Introduction to Machine Learning badge. A printable ��ý certificate will also be available for successful participants.
Recommended next step: Advanced Machine Learning
Who can take this course: This course is open to all engineers, professionals, faculty and students.

ABOUT THE COURSE

This course will provide a solid introduction to machine learning. In particular, upon successful completion of this course, students will be able to understand, explain and apply key machine learning concepts and algorithms, including:

Probability review
Introduction to different types of machine learning and supervised learning
Decision trees algorithm
Naïve Bayes algorithm
Logistic Regression algorithm
Machine learning concepts such as regularization, overfitting, and Laplace smoothing

LEARNING OUTCOMES

At the end of the course, students will be able to:

Understand different types of machine learning and map problems to different classes of machine learning algorithms.
Describe and apply machine-learning algorithms including decision trees, naïve Bayes, and logistic regression.
Understand subtleties and application scenarios for different supervised classification algorithms discussed above.
Explain and apply machine-learning concepts such as regularization, overfitting, and Laplace smoothing to design efficient machine learning models.

ABOUT THE INSTRUCTOR

Arti Ramesh is an assistant professor in the School of Computing at ��ý. She received her PhD in computer science from the University of Maryland, College Park.

Her primary research interests are in the field of machine learning, data mining and natural language processing, particularly statistical relational models and deep learning. Her research focuses on building structured, fair, and interpretable models for reasoning about interconnectedness, structure, and heterogeneity in networked data.

She has published papers in peer-reviewed conferences such as IJCAI, AAAI, ACL, WWW, ECAI, and DSAA. She has served on the TPC/reviewer for notable conferences such as ICML, IJCAI, AAAI, NIPS, SDM, and EDM. She has won multiple awards during her graduate study including the Ann G. Wylie Dissertation Fellowship, outstanding graduate student Dean’s fellowship, Dean’s graduate fellowship, and Yahoo scholarship for Grace Hopper conference.

COURSE FEES

$250: Standard/Industry Rate (group rates available, see below)
$225: Group rate Standard/Industry (3-5 people from the same organization)
$150: BU and SUNY faculty/staff/alumni graduated May 2020 or prior
$105: Non-BU and non-SUNY students (must give evidence of matriculation at University/College)
$95: BU and SUNY Students and recent BU alumni graduated Dec. 2020 or after/High School students.
$35: retake fee Students (requires proof of previous registration)
$50: Retake fee Non-Students (requires proof of previous registration)

Industry Group rate: 3-5 people from the same organization: $225 per person. Contact wtsnindy@binghamton.edu for promo code to use when you register.

PAYMENTS

Payment is made at the time of registration. For questions, contact the Office of Industrial Outreach at wtsnindy@binghamton.edu.

CANCELLATIONS AND REFUNDS

Please note our cancellation and refund policy: All cancellations must be received in writing (email) to the Office of Industrial Outreach. All refunds will be assessed a 10% administrative fee. No refunds for cancellations or non-attendance will be given after you have started the course. Submit your cancellation request to EMAIL: wtsnindy@binghamton.edu.

If the course is canceled, enrollees will be advised and receive a full refund.

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