Vikram Singh – Readings

Linear Algebra and Matrix Theory

Books

• Linear Algebra and Its Applications by Gilbert Strang.

• Linear Algebra Done Right by Sheldon Axler.

Video Sources

• Advanced Matrix Theory and Linear Algebra by Prof. Vittal Rao(IISC).

• Linear Algebra by Prof. Gilbert Strang(MIT).

Probability Theory

Books

• Probability and measure theory by Robert B. Ash.

• A Course in Probability Theory by Kai Lai Chung.

• Probability and Random Processes by Geoffrey Grimmett and David Stirzaker.

• A First Course in Probability by Sheldon Ross.

Video Sources

• Probabilistic Systems Analysis and Applied Probability Prof. John Tsitsiklis(MIT)-Basic.

• Probability Foundations for Engineers by Prof. Krishna Jaggannathan(IIT Madras)-Advance.

Wireless Communication\({^*}\)

Books

Fundamentals :

• Fundamentals of Wireless Communication by David Tse and Pramod Viswanath.

• Wireless Communications: Principles and Practice by Theodore Rappaport.

• Principles of Modern Wireless Communication Systems by Aditya K. Jagannatham.

• Introduction to Wireless Digital Communication: A Signal Processing Perspective by Robert W. Heath Jr..

MIMO and Massive MIMO :

• Foundations of MIMO Communication by Robert W. Heath Jr. and Angel Lozano.

• Fundamentals of Massive MIMO by Thomas L. Marzetta, Erik G. Larsson, Hong Yang, and, Hien Quoc Ngo.

• Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency by \(\mathbf{Emil\;Bj\ddot{o}rnson}\), Jakob Hoydis and Luca Sanguinetti.

Millimeter Wave Communication :

• Millimeter Wave Wireless Communications by Theodore S. Rappaport, Robert W. Heath, Jr., Robert C. Daniels and James N. Murdock.

Video Sources

• Advanced 3G & 4G Wireless Mobile Communications by Prof. Aditya Jagganathan(IIT Kanpur).

• Introduction to Wireless and Cellular Communications by Prof. R. David Koilpillai (IIT Madras).

Research Links

• NYU Wireless New York University(NYU) for mmWave.

• WSIL and Jeff Andrews's Group UT Austin.

• Communication Systems(KS) Linkoping University.

• Networks at MIT.

Convex Optimization\({^*}\)

Books

• Convex Optimization by Stephen Boyd and Lieven Vandenberghe.

• Convex Optimization Theory by Dimitri Bertsekas.

Video Sources

• Convex Optimization-I by Prof. Stephen Boyd(Stanford).

• Convex Optimization by Prof. Ryan Tibshirani(CMU).

Research Links

• Stephan Boyd page.

• Dimitri Bertsekas Page.

• I will add as soon as I know anything else.

Detection and Estimation Theory\({^*}\)

Books

• Principles of Signal Detection and Parameter Estimation by Bernard C. Levy.

• Detection Estimation and Modulation Theory, Part I: Detection, Estimation, and Filtering Theory Adaptive Filters by Harry L. Van Trees, Kristine L. Bell and Zhi Tian.

• Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory and Volume II: Detection Theory by Steven M. Kay.

Adaptive Signal Processing\({^*}\)

Books

• Adaptive Filters: Theory and Applications by Behrouz Farhang-Boroujeny.

• Adaptive Filters by Sayed H Ali.

• Adaptive Filter Theory by Simon Haykin.

Machine Learning\({^\dagger}\)

Books

• Pattern Recognition and Machine Learning (PRML) by Christopher Bishop.

• Machine Learning: A Probabilistic Perspective (MLAPP) by Kevin Murphy.

• Information Theory, Inference and Learning Algorithms(IILA) by David J. C. MacKay.

• A Course in Machine Learning(ICML) by \(\mathbf{Hal\;Daum\acute{e}}\) III.

• Deep Learning(DL) by Ian Goodfellow, Yoshua Bengio and Aaron Courville.

Video Sources

• Machine Learning by Andrew Ng

• Introduction to Computational Thinking and Data Science(MIT).

• Machine Learning Specialization by University of Washington(UW).

Research Links

• Machine Learning Group(MIT).

• Berkeley Artificial Intelligence Research Lab(UC Berkeley).

• Stanford Artificial Intelligence Laboratory - Machine Learning.

• Machine Learning Department(CMU)

Deep Learning\({^\dagger}\)

Books

• Deep Learning by Ian Goodfellow et.al.

• Neural Networks and Deep Learning by Michael Nielsen.

Columns

• Andrej Karpathy blog.

Video Sources

• Deep Learning Specialization by Andrew Ng.

• Deep Learning by Ian Goodfellow.

• MIT Deep Learning

• Deep Learning for NLP(Stanford). and many more

Reinforcement Theory \({^\ddagger}\)

Books

• Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto.

• Algorithms of Reinforcement Learning by \(\mathbf{Csaba\;Szepesv\acute{a}ri}\).

Columns

• Two very thorough answer by Parnian Barekatin about Kick starting AI and Reinforcement Learning on Quora.

Video Sources

• Advanced Deep Learning and Reinforcement Learning by DeepMind.

• Reinforcement Learning-CS231 by Emma Brunskill(Stanford).

• Deep RL Bootcamp by Peter Abeel(UCB).

• Deep Reinforcement Learning by Sergey Levine(UCB).

Probabilistic Graphical Models\({^\dagger}\)

Books

• Probabilistic Graphical Models : Principles and Techniques by Daphne Koller and Nir Friedman.

• Machine Learning: A Probabilistic Perspective (MLAPP) by Kevin Murphy.

Video Sources

• Probabilistic Graphical Models by Daphne Koller(Coursera).

Quantum Computing\({^*}\)

Books

• Quantum Computation and Quantum Information, by Nielsen and Chuang

• Lecture notes on Quantum Computation by John Preskill.

Video Sources

• Quantum Mechanics and Quantum Computation by Umesh V. Vazirani (edX)

• Quantum Information Science II, Part 1 - Quantum states, noise and error correction by Peter Shor and Isaac Chuang (edX).

• Quantum Information Science II, Part 2 - Efficient Quantum Computing - fault tolerance and complexity by Isaac Chuang and Aram Harrow (edX).

• Quantum Information Science II, Part 3 - Advanced quantum algorithms and information theory by Isaac Chuang and Aram Harrow (edX).

\(^*\) : This subject require you to be good at both Linear algebra and Probability Theory.
\(^\dagger\) : This subject assumes proficiency in not only Linear algebra and Probability Theory but Convex Optimization also.
\(^\ddagger\) : Knowledge of Machine learning is important and Game theory is bonus but not necessary.