Semester Project Presentations: Wednesday, December 6 in Clark Hall 118 (10:30 am - 2:30 pm).

Course and Contact Information

Instructor: Saptarshi Sengupta, PhD

Office Location: MacQuarrie Hall 416

Telephone: 408-924-4808

Email: saptarshi.sengupta@sjsu.edu

Office Hours: Thursday, 1:00 PM – 3:00 PM

Class Days/Time: MW 10:30 AM - 11:45 AM

Classroom: Duncan Hall 450

Prerequisites: CS 146 (with a grade of "C-" or better)

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San José State University
Department of Computer Science

Course Description:

The course is an in-depth exploration of the underlying mathematics as well as some practical applications in the field of machine learning. The course also focuses on designing algorithms that allow computers to learn and take actions in a probabilistic fashion using statistical inferences. Students will explore Probabilistic Reasoning, Belief Networks, Bayesian learning, Hidden Variable Learning, Supervised and Unsupervised Learning, Linear Modeling, Gaussian Processes, Mixture Models, Discrete State Markov Models. In addition, students will explore the fundamentals and various use cases in Deep Learning, Natural Language Processing and Reinforcement Learning using packages such as Scikit-Learn, Tensorflow, Keras, PyTorch, SpaCy, NLTK etc.

Course Learning Outcomes (CLO):

The focus of this course will be machine learning, with examples from fields such as computer vision, anomaly detection, health informatics and prognostics. After completing this course students should have a working knowledge of a wide variety of machine learning topics and have a good understanding of how to apply such techniques to real-world problems in the industry.

Required Texts/Readings:

Textbook

Machine Learning: An Algorithmic Perspective, Second Edition, 2014, Chapman and Hall/CRC

Authors: Stephen Marsland

ISBN-13 : 978-1-4665-8333-7 (eBook - PDF)

Other Readings:

Machine Learning with Applications in Information Security, by Mark Stamp, published by Chapman Hall/CRC in 2017.

ISBN-10 : 1138626783

ISBN-13 : 978-1138626782

Deep Learning (Adaptive Computation and Machine Learning series)

Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville

ISBN-13: 9780262035613

ISBN-10: 0262035618

Other technology requirements / equipment / material:

Python 3, Scikitlearn libraries, numpy/scipy, tensorflow/keras, gym, Jupyter notebooks among others. Installing Anaconda is highly recommended. I will be using Jupyter Notebook in my code demos in class.