Educational Resources

I used the following resources to educate myself in the past years. Some editions of the textbooks listed below might be old. Try to find the newest editions to be up-to-date with latest advancements. The lists are written in alphabetical order.

Online Courses

Artificial Intelligence

• Artificial Intelligence [MIT OpenCourseWare]
• Deep Learning Specialization [deeplearning.ai @ Coursera]
1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models
• Introduction to Reinforcement Learning [DeepMind]
• Machine Learning [Stanford University @ Coursera]
• Probabilistic Machine Learning [University of Tübingen]
• Statistical Machine Learning [University of Tübingen

Causal Inference

• Mini course on Causality by Jonas Peters
• Part 1
• Part 2
• Part 3
• Part 4

Computer Science

• Algorithms: Design and Analysis (Part 1) [Stanford University]
• Algorithms: Design and Analysis (Part 2) [Stanford University]
• Computer Organization [Bilkent University]
• Design and Analysis of Algorithms [MIT OpenCourseWare]
• Introduction to Algorithms [MIT OpenCourseWare]
• Operating Systems [Bilkent University]

Mathematics

• Convex Optimization [Stanford Univesity]
• Linear Algebra [MIT OpenCourseWare]
• Mathematics for Computer Science [MIT OpenCourseWare]
• Probabilistic System Analysis and Applied Probability [MIT OpenCourseWare]
• Probability [Harvard]

Neuroscience

• Medical Neuroscience [Duke University @ Coursera]

Textbooks

Artificial Intelligence

• Stuart J. Russell, Peter Norvig. Artificial Intelligence: A Modern Approach (3rd Edition). Pearson Education, 2010.
• Jiawei Han, Micheline Kamber, Jian Pei. Data Mining: Concepts and Techniques (3rd Edition). Elsevier Inc., 2011.
• Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning. MIT Press, 2016.
• Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
• Richard S. Sutton, Andrew G. Barto. Reinforcement Learning: An Introduction (2nd Edition). MIT Press, 2018.
• Dan Jurafsky, James H. Martin. Speech and Language Processing (2nd Edition). Pearson, 2014.

Causal Inference

• Judea Pearl, Madelyn Glymour, Nicholas P. Jewell. Causal Inference in Statistics: A Primer. Wiley, 2016.
• Jonas Peters, Dominik Janzing, Bernhard Schölkopf. Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press, 2017.

Computer Science

• David A. Patterson, John L. Hennessy. Computer Organization and Design: The Hardware/Software Interface (4th Edition). Morgan Kaufmann, 2012.
• Ramez Elmasri, Shamkant Navathe. Fundamentals of Database Systems (7th Edition). Pearson Education, 2016.
• Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein. Introduction to Algorithms (3rd Edition). MIT Press, 2009.
• Abraham Silberschatz, Greg Gagne, Peter B. Galvin. Operating System Concepts (9th Edition). Wiley, 2013.

Mathematics

• Mark Pinsky, Samuel Karlin. An Introduction to Stochastic Modeling (4th Edition). Academic press, 2011.
• Howard Anton, Irl Bivens, Stephen Davis. Calculus: Early Transcendentals (10th Edition). Wiley, 2012.
• Stephen Boyd, Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
• Kenneth H. Rosen. Discrete Mathematics and Its Applications (7th Edition). Tata McGraw-Hill Education, 2012.
• Thomas M. Cover, Joy A. Thomas. Elements of Information Theory (2nd Edition). John Wiley & Sons, 2006.
• Gilbert Strang. Introduction to Linear Algebra (4th Edition). Wellesley-Cambridge Press, 2009.
• Frederick S Hillier, Gerald J. Lieberman. Introduction to Operations Research (10th Edition). Tata McGraw-Hill Education, 2015.
• Dimitri P. Bertsekas, John N. Tsitsiklis. Introduction to Probability. MIT, 2000.
• Hefferon, J. Linear Algebra (3rd Edition). 2017.
• Morris H. DeGroot, Mark J. Schervish. Probability and Statistics (4th Edition). Pearson Education, 2012.

Neuroscience

• Dale Purves, et al. Neuroscience (6th Edition). Oxford University Press, 2018.

Programming

• Walter J. Savitch, Kenrick Mock. Absolute Java (5th Edition). Pearson Education, 2013.
• Stuart C. Shapiro. Common LISP: An Interactive Approach. Computer Science Press, 1992.
• Harvey Deitel, Paul Deitel. How to Program: C. Pearson Education, 2013.