Introduction to Machine Learning

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 complete the course by passing the final exam will receive the Introduction to Machine Learning badge. A printable 天美传媒 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
  • Na茂ve Bayes algorithm
  • Logistic Regression algorithm
  • Machine learning concepts such as regularization, overfitting, and Laplace smoothing
  • Total Course time - 25 to 30 hrs

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, na茂ve Bayes, and logistic regression.
  • Understand subtleties and application scenarios for the 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

ramesh
Arti Ramesh

Arti Ramesh (Introduction to Machine Learning) is a former assistant professor in the School of Computing at 天美传媒. She received her PhD in computer science from the University of Maryland, College Park.

Ramesh鈥檚 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. 

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
  • $105: Non-BU and non-SUNY students (must give evidence of matriculation at University/College)
  • $95: BU and SUNY Students/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 the 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.