Classes at the forefront of technology 6.036 Intro to Machine Learning Instructors: Regina Barzilay, Tommi Jaakkola
6.036 Intro to Machine Learning, continued Students in 6.036 will have another meaningful experience when current researchers come to talk to the class about their work and how they apply machine learning. Sheena remembers one such talk last spring on the topic of robot planning problems. Fascinated by the topic, she introduced herself following the class to the speaker and landed a UROP in his group. Overall, she says, the class has given her the foundation to tackle problems in data science and robotics.
Until a year ago, undergraduates in Course 6 at MIT didn’t have a chance to study this blossoming field unless they wanted to join an advanced and large graduate class (6.867) that has an imposing list of pre-requisites. So, with the motivation that EECS (and a wide swath of MIT) students, regardless of their trajectory, will encounter machine learning in the course of their careers, Intro to Machine Learning was first offered in spring 2013. Today, 6.036 has become a magnet for engineering and computer science students. Nearly 300 students fill 10-250 twice a week to take in the 6.036 lectures by Professors Regina Barzilay and Tommi Jaakkola. MEng. student Eric Shuy, one of five TAs for this year’s offering, took the class last year. Now he is enjoying the chance
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to learn more by teaching others. Besides motivating him to explore the subject more deeply through classes, online resources, and research, upcoming 6.036 programming projects in the class give Eric and the students a chance to get real-time exposure. Among the projects, students will be applying the machine learning principles they’ve studied to real problems such as predicting the sentiment of a Twitter post. Shuy suggests that the class goes over so well because the students get to apply what they learn to problems that they care about. Xinkun (Sheena) Nie, took the class last year and is very enthusiastic about what she learned and how much she enjoyed the class. “The best parts of the class [were] the assignments and projects,” she says. She still remembers that the projects involving facial recognition and Tweet classification were both fun and challenging to implement. “[This] hands-on component of the class definitely helped me conceptually understand the materials,” she says, “and made it stand out among the other Course 6 classes I was taking at the time.” During the summer following her taking the class, Sheena Nie interned at Hunch, a machine learning startup that had been acquired by eBay. It was very exciting for her to apply what she had learned in Intro to Machine Learning to realworld industrial applications.
Figure 2: Predicting Tweet Sentiments
Machine Learning is everywhere! At least, this statement is believable when you realize how this discipline can be applied to predict answers to important questions in just about any real world situation that involves data. Machine learning algorithms are being used to automate many data associated needs such as recognizing the signs of cancer, predicting stock prices, translating languages, detecting credit card fraud, recommending movies — and the list goes on. All these instances involve questions requiring predictions driven by data – extremely challenging for individuals to specify or know exactly how to solve each situation.
For the second project (Figure 2 below), students classify a Twitter tweet as being a negative or a positive movie review. Students used the Perceptron and Passive-aggressive algorithms to learn how strongly particular words are related to a positive or negative sentiment.
Figure 1: Dimensionality Reduction
Two project examples are illustrated below. The first project involved applying dimensionality reduction, clustering, and classification algorithms to face images. Students used the Principle Component Analysis algorithm to find lower-dimensional approximations of the images—e.g. in the figure with 10x dimensionality reduction, the right images have about 10x
less data (thus requiring 10x less space] but still manage to approximate the left images pretty well. By implementing the K-means algorithm, the students were able to automatically find clusters of similar face images. They also used Support Vector Machines to automatically distinguish between different face images of two different people. (Figure 1 below)
MIT EECS Connector — Spring 2014
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