This semester, our Columbia Statistical Club will start a new study group – AI Study Group!
Description:
-Objective: Understanding the broad concept of AI, Deep Learning and Machine Learning, cooperating with others in group studying, contributing and learning from different perspectives.
-Requirement: If you have the passion to explore various topics and cutting-edge techniques in AI, Deep Learning, Machine Learning, the dedication to work closely with others in a study group format in tackling challenging questions, the project-oriented CSC AI Study Group will provide you with the perfect platform. Programming experience is preferred but not required.
The AI Study Group consists of two parts:
Part 1: Study Group
1. Form a team of 4-5 people
2. Tackle a question/topic given by organizers from different angle as a team
Part 2: Guest Speaker Session
1. Study Session (1hour)
2. Speaker Presentation (30 minutes)
3. Speaker Q&A (15 minutes)
4. Possible networking opportunity
Details:
Part I: AI Study Group
Time and Location:
-Every Saturday 1:30-3:30PM
-Starting 5th week Saturday 10/7
-Location: Hamilton
Self – Learning study group with project topics and learning resources provided. Students are expected to learn and explore the foundation of AI by building the neural network (NN) and compare to other machine learning techniques. Members who work on projects will get the gift card as award and will have a higher chance of interacting with potential employers. They will also have more materials to add to their resumes and discuss at interviews. (Limited spaces)
If you are interested in the AI study group, you can register here:
Show your interest registration
Note: When you register for this particular event, you show your interest in the AI study group, but we do not guarantee you a spot in the study group. We will have an admission test to decide whether you are suitable for this group. Once you pass the test, you are committed to the self-learning study group, meaning working on the projects that the study group assigns to you. Also you are automatically registered for all lecture sessions.
For the self-learning study group, we’ll have regular meeting every Saturday afternoon from 1:30-3:30. These two hours are designated for lectures, discussion and presentation on the projects.
Here is the AI Study Group syllabus: http://blogs.cuit.columbia.edu/statisticsclub/fall-ai-study-group-2017-syllabus/
Part II: Guest Speakers
A series of sharing sessions from industry experts covering AI related topics (open to all club members who have interests, even if for those who do not register for the AI study group. We’ll have seperate links to register for sharing sessions).
Guest Speaker 1: Arun Tripathi, Ph.D. LinkedIn
Time: 10/7 2:30 – 3:30
Location: TBA
What is Machine Learning
A Brief history
Current Landscape
AI & Deep Learning
Some business applications
Future trends
Personal thoughts on navigating through the universe of machine
learning
Registration: Dr. Arun Tripathi Presentation
Dr.Arun Tripathi Presentation Slides
Guest Speaker 2: Lucas Lau LinkedIn
Time: 10/14
Topic: Introduction to Artificial Intelligence
About the speaker:
Club Mentor (AI Study Group)
Lucas is an experienced analytics leader with established track records of delivering professional analytics services, research publications and public speaking. He has extensive experience working with C-Suite senior executives to design and execute business analytics strategies and solutions.
As a thought leader and frontier analytics practitioner, Lucas has been working on projects that adopted cutting edge technology and advanced machine learning algorithms such as deep learning. His strength is enabling business organizations with advanced analytics and cognitive computing solutions that deliver high business values and translating complex analytics to user-friendly context that ensure seamless implementation and smooth change management.
Guest Speaker 3: Viola Cao
Time: 10/21
Topic: Pokemon Generative Adversarial Networks(GAN)
About the speaker:
Current Position: Data Scientist at Zurich North America
Backgrounds: Data Science Master Candidate at Center of Data Science established by Yann LeCun at New York University, former Data Scientist at United Nations, former Research Assistant at Stanford University’s Statistics Lab.
Her specialties are Deep/Machine Learning in Computer Vision and Natural Language Processing, Generative Adversarial Networks, Content-based Algorithms, Geo-Temporal modeling and Anomaly Detections. She has rich experiences in new technology, media, medical, infrastructure and financial industries.
Guest Speaker 4: William Li
Time: 10/28
Deep learning, Nanotechnology, and Hardware Acceleration
Rather than erupting overnight, deep learning research and development can be traced back to 1970s or earlier. In this talk, we will investigate what prevented deep learning from gathering momentum, and how nanoscale semiconductor technology has fueled the recent exponential growth of machine learning and artificial intelligence applications. We will then give an overview of the different types of hardware used for machine learning, and conclude with future outlook from a technology perspective.
Biography
William Li is an engineering manager at Intel Corporation. After receiving his PhD in Electrical Engineering from Columbia University, he joined Intel focusig on nanoscale circuit and system research. Recently, he has been engaging with hardware acceleration utilizing FPGA for networking, cloud, and machine learning. His research interest includes energy-efficient computing, digital signal processing, and machine learning algorithm and implementation.