From Monash to UC Berkeley
Study at Monash
“Ancora imparo”
Monash University, founded in 1958, is located in the state of Victoria in southeastern Australia and is named after Sir John Monash, an Australian military leader and engineer. The university is known for the quality of its teaching and its exceptional facilities and it is also a member of Australia’s Group of Eight [1].
I’m the student enrolling Master of data science at Monash University. After one year study in here, Monash IT faculty provided me with an opportunity to study at the school of information at UC Berkeley for one semester, and I was lucky to be selected as the candidate with sponsorship.
Study at School of Information, UC Berkeley
“Let there be light”
South Hall (School of Information)
For every year, the school of information admits round 50 students for MIMS program, so each student has the opportunity supervised by one faculty member. Additionally, ISchool usually holds many sociable activities to help students knowing each other, and provides many academic seminars or working opportunities for students. I used to attend the seminars such as Dean’s Lecture, Dean’s Dinner, and Friday afternoon seminar.
Dean’s lecture invites the scholars from other university or organizations to share their research results, and opinions.
Dean’s Dinner usually invites former government officers such as former White House Presidential Innovation Fellow, former Deputy U.S. Chief Technology Officer, former U.S. Chief Data Scientist to discuss some themes in the view from government. Since this is dean’s dinner seminar, every guest will be provided with food and wine.
My experience for completed classes at ISchool
INFO 271B Quantitative Research Method (instructed by Professor Coye Cheshire).
Actually, it should be named “Learning statistics in a happy and easy way” or “no pain to learn statistics”. This is a very popular class at ISchool only provided in the fall semester. Professor Cheshire demonstrates the concept with plain English (ps. most of the time demonstrate the concept with jokes hhh).
From experiment design to inferential statistics to model diagnostics, you will be taught by using many vivid examples. This class teaches you how to do a scientific research from proposing problems to test your hypothesis about this problem, helps you to tell the difference between correlation and causality, diagnosing linear regression model and so on. All the statistical problems will be demonstrated step by step by using R first, and then distribute relevant homework for you to familiarize the concepts.
INFO 251 Applied Machine Learning (instructed by Professor Joshua Blumenstock)
This course combined experimental econometrics (Part I) and machine learning (Part II) which could be summarized as a bridging course from descriptive and inferential statistics (info 271B) to the world of Machine Learning. You will be taught with some core techniques of econometrics such as experimental principle and methods in Part I. Then, part II will teach you the algorithms and applications of Machine learning. For the homework, students are required to implement all the most common machine learning algorithms from scratch, and this class also sets many challenging questions as extra credit for the students who would like to learn more. Info 251 also invites many domain area experts to teach us about their research areas such as reinforcement learning, feature engineering, and optimal treatment rule. Tips: After completing this class, you are able to start with some projects or competitions like Kaggle to apply what you have learned to solve practical problems.
INFO 290 Sensor, Human, Data, Application (instructed by Professor John Chuang)
This is an unrestrained class, focusing more on creativity from yourself. Students should think about the ideas of all the homework by themselves, and then complete your proposed homework with a report of the experimental results. You will be taught different types of bio-sensory signals such EEG, EMG, ECG and so on. The instructor can provide you with one or two sensors to collect the bio-sensory data from yourself, and analyze it to conclude the patter from your body. You can use whatever you have learned from other classes such as info 271B and info 251 to analyze your bio-signals to find significant results.
By the way, since Professor John Chuang and Coye Cheshire are working in the same lab, many times they will use the research from each other as demonstrated examples in lecture.
INFO 259 Natural Language Processing (instructed by Professor David Bamman)
This class will provide you with a comprehensive view of Natural Language Processing, covering most topics in this area such as language model, seq-seq model, syntax parsing, coreference resolution, and so on. Honestly, it needs info 251 or equivalent machine learning class as pre-requisite because this course basically concentrates on using deep learning and cluster algorithms to analyze text data. If you enroll the graduate version (info 259 instead of info 159), you are required to complete a semester-long project, which will go through from brainstorming ideas, writing project proposal, collecting data, analyzing data, and write a conference standard paper. If you make lots of efforts in this class, you might be so lucky to obtain a published paper in a course.
Ways for Students Exhibiting their Projects
1. Project Demo
There are many classes such as Info 262 in ISchool providing opportunities with students to display and demonstrate the product that they did for the course requirement to visitors.
2. Project Poster
Then, some classes such as Info 259 will hold a project poster exhibition for anyone to visit and ask questions. By the way, if you are the presenter and also interested in other student’s project, you are also welcomed to visit their projects.
3. Project Presentation
Finally, we come to the usual way to present your idea that is to give a speech in front of the audiences. Normally, there are two parts: 2/3 of time for presentation and the other time for question answering.
This is a precious memory for me to explore a new environment enriched with experiencing the stress of 6 homework due in one week, the happiness of obtaining full marks for many homework, and the recognition from the instructor. There are so many lovely and friendly people in ISchool, and you won’t feel alone in here. I gained a lot of insights and knowledge from the professors and students in here. I appreciate that all the ISchool members accompany and help me to advance myself. This is very impressive studying experience in my life. Finally, I really appreciate that my home department Monash University selected and sponsored me to study at UC Berkeley.
Bonus
The dean of ISchool is very amiable and approachable. You may meet her during the orientation week, in the dean’s seminar, in the queue when buying a coffee, and sometime on the screen when you are in the plane on the way back to your home^_^
Reference
[1] https://www.monash.edu/about/who/history
[2] https://www.ischool.berkeley.edu/about/history
