[MLSOP R2] - Efficient Processing of Deep Neural Networks - Vivienne Sze

Recommended by seniors

[MLSOP RX] - Distributed and Cloud Computing - Kai Hwang

Required chapters for ML Sys Ops course.

[MLSOP R1] - Automated Machine Learning - Adnan Masood

This course has no official textbooks, these textbooks here are recommended by seniors

[DNN R2] - Introduction to Deep Learning by Eugene Charniak

Full Book - For subject DNN - R2 reference book

[DNN R1] - Deep Learning by Ian Goodfellow

All required chapters required for DNN syllabus until chapter 11 practical methodology

[DNN T1] - Dive into Deep Learning - Aston Zhang

All required chapters from the textbook T1 Dive into Deep Learning by Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola For Deep Neural Network Semester 2 mandatory subject

[DRL T2] - Foundations of Deep Reinforcement Learning

T2 textbook of DRL subject until chapter 13 as per handout

[DRL T1] - Reinforcement Learning: An Introduction, Richard S. Sutton

All required chapters from - mandatory subject Deep reinforcement learning - T1 Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, Second Ed. , MIT Press

[DMML Reference Compilation] - R1 + R3 - As per handout

DMML Reference Compilation - R1 + R3 - As per handout

[DMML T1] - Fundamentals of Data Engineering Plan and Build Robust Data Systems

T1 Textbook for DMML elective

[DMML T2] - Reliable Machine Learning: Applying SRE Principles to ML

Textbook T2 for elective DMML

[IR Reference Compilation] - R1 + R2 + R3 + R4

Compilation of all required chapters from IR reference books as per handout. R1 + R2 + R3 + R4

[IR T1] - Introduction to Information Retrieval - Christopher D. Manning

All required chapters from elective Information Retrieval - IR T1 - Introduction to Information Retrieval by Christopher D. Manning

[PGM T2] - Building Probabilistic Graphical Models with Python

[PGM T1] - Mastering Probabilistic Graphical Models Using Python

T1 textbook of elective PGM

[FaAct T2] - Interpretable Machine Learning

Full textbook of Interpretable Machine Learning - T2 - Elective FaAct

[FaAct T1] - Fairness and machine learning

T1 textbook for elective FAccT Machine Leaning

[NLP T1] - Jurafsky and Martin SPEECH and LANGUAGE PROCESSING

All required chapters from NLP T1 textbooks

[NLP T2] - Christopher D. Manning and Hinrich Foundations Of Statistical NLP

All required chapters from NLP T2 textbook

[CS T1] - Data Mining and Machine Learning in Cybersecurity

T1 textbook for AI and ML Techniques for Cyber Security

[CS T4] - Hands-On Machine Learning for Cybersecurity

Full book of T4 - Hands-On Machine Learning for Cybersecurity - For elective Cyber Security

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