Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
Tips for would-be founders, technical or non-technical, before rolling up their sleeves and develop their products! From various ways of "pretotyping" to accurately gauge target customer's response, lean method, minimum viable product, feature selection, planning a product with robust data cycle, coping with delays, and guiding a team of rockstar engineers to build the right product and build the product right. Some personal experienced shared at the end as case studies.
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180526@Taiwan AI Academy, Professional Managers Class.
Covering important concepts of classical machine learning, in preparation for deep learning topics to follow. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
Find Your Passion and Make a Difference in Your CareerAlbert Y. C. Chen
20180314 at National Taiwan Normal University.
Reflection on my own career from being inspired to work on CV/ML research during my graduate studies at NTNU, then going abroad to obtain my Ph.D. and later on my career in this field. The talk emphasizes on the importance of innovation and how to realize ones new ideas within large and small organizations.
Covering important topics of Classical Machine Learning in 16 hours, in preparation for the following 10 weeks of Deep Learning courses at Taiwan AI academy from 2018/02-2018/05. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
AI gold rush, tool vendors and the next big thing
2017/12/27 at Mediatek
- Overview of booming AI applications, from media, entertainment, e-commerce, autonomous driving, surveillance, industrial inspection, medical imaging, bioinformatics, finance, etc., along with expert predictions of their market size and growth.
- Dissect the applications with largest size and growth into their technical components and their unmet demands.
- Among all the unmet demands and uncertainties in this AI gold rush, what should an IC design company do? I’ll briefly cover NVIDIA’s case, which most of us know well already, then supplement case studies of Qualcomm, Intel, Google TPU and other smaller firms.
Even when we have a clear target, it takes years for supporting libraries and software to be properly optimized. I’ll share some thoughts and personal experiences on how to make sequentially-ordered hardware/software/library optimization happen faster and in parallel, and the tools that the IC design house need to provide in order for it to happen.
Practical computer vision-- A problem-driven approach towards learning CV/ML/DLAlbert Y. C. Chen
Practical computer vision-- A problem-driven approach towards learning CV/ML/DL
Albert Chen Ph.D., 20170726 at Academia Sinica, Taiwan
Invited Speech during Academia Sinica's AI month
Think different, in Finance. An outsider's two cents on how could finance majors rethink their role and value in the rapidly changing AI era, with some FinTech case studies.
Albert Y. C. Chen, Ph.D., VP of R&D at Viscovery--Visual Search, Simply Smarter.
Invited speech at Automatic Optical Inspection Equipment Association (AOIEA) Annual Summit, Taiwan, 2017/06/15, "Deep Learning and Automatic Optical Inspection".
陳彥呈博士,Viscovery研發副總裁2017年6月15日於自動光學檢測設備聯盟 會員年會 專題演講「人工智慧下的AOI變革浪潮:影像辨識技術的突破與新契機」。