Courses&Books

Courses at UMass

• WHY UMASS ROCKS

• Courses for MS (30 Credits)

• AI Core (3 Courses/9 Credits)

  • CMPSCI 646 Information Retrieval (Fall 2014) by Prof. James Allan

  • CMPSCI 683 Artificial Intelligence (Fall 2014) by Prof. Shlomo Zilberstein

  • CMPSCI 689 Machine Learning (Spring 2015) by Prof. Subhransu Maji

• System Core (2 Courses/6 Credits)

  • CMPSCI 645 Database Design and Implementation (Spring 2015) by Prof. Alexandra Meliou

  • CMPSCI 620 Advanced Software Engineering: Synthesis and Development (Fall 2016) by Prof. René Just

• Theory Core (1 Course/3 Credits)

  • CMPSCI 611 Advanced Algorithms (Fall 2015) by Prof. Andrew McGregor

• One 600-Seminars and One Independent Study(2 Course/6 Credits)

  • COMPSCI 690OP Optimization in Computer Science (Spring 2016) by Prof. Sridhar Mahadevan

  • COMPSCI 690OP Optimization in Computer Science (Spring 2019) by Prof. Arya Mazumdar (TA for this course in Spring 2019 with Prof. Arya Mazumdar)

  • CMPSCI 590N Introduction to Numerical Computing with Python (1 credit for Python and DL research, Fall 2016) by Roy Adams

  • CMPSCI 696 Independent Study (Spring 2016)

• Master Project/Synthesis Project (6 Credits)

  • CMPSCI 701 Master Project In Combination with Synthesis Project(Propose in Spring 2016/ Finish in Fall 2016)

• Additional Courses for MS/PhD (2 600-Seminars/6 Credits)

• • CMPSCI 697L Deep Learning (Fall 2015 Version) by Prof. Sridhar Mahadevan

  • CMPSCI 697L Deep Learning (Fall 2016 Version) by Prof. Erik Learned-Miller

  • CMPSCI 682 Deep Learning (Fall 2017 Version) by Prof. Erik Learned-Miller

  • CMPSCI 682 Deep Learning (Fall 2018 Version) by Prof. Erik Learned-Miller

  • CMPSCI 690D Deep Learning for Natural Language Processing (Spring 2019) by Prof. Mohit Iyyer and Brendan O'Connor

  • Stanford CS231n Convolutional Neural Networks for Visual Recognition Course Website Course Video on Youtube Spring 2017 Video by Prof. Fei-Fei Li, Dr. Andrej Karpathy, Dr. Justin Johnson

  • Stanford CS224d Deep Learning for Natural Language Processing Course Website CS224d Course Website CS224n Course Video on Youtube (2016) Course Video on Youtube (2017) by Dr. Richard Socher and Prof. Chris Manning

    • CS 224n Natural Language Processing with Deep Learning by Prof. Chris Manning Winter 2019 Winter 2019 Youtube Video

    • CS 230 Deep Learning by Prof. Andrew Ng Fall 2018

    • CS 234 Reinforcement Learning Winter 2019

    • CS 224u Natural Language Understanding by Prof. Christopher Potts and Dr. Bill MacCartney Spring 2019 Youtube Video

  • Amazon Online Course Learning Deep Learning by Coding Practice Course Video on Youtube Course Note/Forum by Dr. Mu Li and Dr. Aston Zhang Book and Course Website

  • UCL COMPM050 Reinforcement Learning Course Website RL in UCL Course Video on Youtube by Prof. David Silver (UCL & Google DeepMind)

    • Youtube Video on Deep Reinforcement Learning MIT Stanford UCL

  • CMPSCI 696 Independent Study (Spring 2017)

  • CMPSCI 677 Distributed and Operating Systems by Prof. Prashant Shenoy Syllabus Lecture notes, handouts and schedule Youtube Videos Textbook

  • CMPSCI 691RS: Introduction to Recommender Systems by Prof. Yongfeng Zhang (Spring, 2017)

  • Stanford Machine Learning Course Youtube Video Link Prof. Andrew Ng

• Other Candidate Courses

  • CMPSCI 688 Probabilistic Graphical Models

  • CMPSCI 690N Advanced Natural Language Processing

  • CMPSCI 653 Advanced Computer Networking

  • CMPSCI 621 Advanced Software Engineering: Analysis and Evaluation

  • CMPSCI 691NR Seminar - Neural Networks-An Introduction

  • Research Methods in Empirical Computer Science (seminar)

  • Data Mining through Grid Computing (seminar)

  • Advanced Machine Learning (seminar)

  • Topics in IR: Current Research Trends in IR (seminar)

  • Topics in IR: Query Modeling and Understanding (seminar)

• Computation+Language at UMass edited by Prof. Brendan O’Connor

Course Projects at UMass

• AISudokuSolver: A Sudoku solver which includes the implementation of plain back tracking, backtracking with the MRV heuristic, AC3 inference, Naked Pair inference, Shared Subgroup inference method and difficulty assessment.

• AISearch

• AIMDPValueIteration

• MLTwoLayerNeuralNetworks

• OPSGD

• OPNewtonMethod

• OPMovieLensMatrixFactorization

• OPMovieLensSingularValueThresholding

• OPPMF ref

• OPNMF

• OPBPMF ref

• OPBPTF ref

• To be added.

Coursera and Online courses

• Dive into Deep Learning by Dr. Mu Li and Dr. Aston Zhang in AWS Deep Learning

• Machine Learning by Prof. Andrew Ng in Stanford University[Learnt]

• Machine Learning by Prof. Thorsten Joachims in Cornell University

• Introduction to Machine Learning by Prof. Alex Smola in Carnegie Mellon University

• Scalable Machine Learning by Prof. Alex Smola in Carnegie Mellon University

• Machine Learning Course Youtube Videos of Prof. Alex Smola in Carnegie Mellon University

• Machine Learning & Deep Learning Course Youtube Videos of Prof. Nando de Freitas in Oxford University

• Statistical Machine Learning by Prof. Michael I. Jordan in UC Berkeley

• Statistical Learning by Prof. Trevor Hastie in Stanford University

• Probabilistic Graphical Model by Prof. Daphne Koller in Stanford University[Learnt]

• Probabilistic Graphical Model Video by Prof. Eric Xing in Carnegie Mellon University

• Natural Language Processing by Prof. Michael Collins in Columbia University[Learning]

• Stanford NLP Course in Youtube Coursera by Prof. Dan Jurafsky & Chris Manning in Stanford University[Learning]

• MIT NLP Course Basic & Advanced by Prof. Regina Barzilay in MIT

Great Talks&Lectures

• Support Vector Machines in MLSS by Prof. Chih-Jen Lin in National Taiwan University

• MXNet/Gluon Video Tutorial by Dr. Mu Li in Amazon

• Stanford CS20 Tensorflow for Deep Learning Research by Chip Huyen and Prof. Christopher Manning from Stanford University

• Research Talks from Jiangmen Bilibili Video

Books

• Dive into Deep Learning by Dr. Mu Li and Dr. Aston Zhang in AWS Deep Learning

• Pattern Recognition and Machine Learning[Reading]

• Probabilistic Graphical Models[Reading]

• Pattern Classification

• Machine Learning[Read]

• Bayesian Reasoning and Machine Learning

• Machine Learning - A Probabilistic Perspective

• The Elements of Statistical Learning

• Speech and Language Processing

• Foundations of Statistical Natural Language Processing

• Mining Text Data[Reading]

• Introduction to Information Retrieval[Read]

• Search Engine Information Retrieval in Practice[Reading]

• Data Mining Concepts and Techniques[Read]

• Graphical Models, Exponential Families and Variational Inference

• Books Recommended by Dr. Dahua Lin

• Statistical Learning Methods(统计学习方法) by Dr. Hang Li