Advanced topics in Artificial Intelligence, including induction, decision trees, ensemble learning; current- best-hypothesis search, knowledge representation, explanation-based learning, relevance information, inductive logic programming; Bayesian networks, instance-based learning; neural networks and genetic algorithms; reinforcement learning, and adaptive dynamic programming.
Common supervised learning applications include:
- Predictive analysis based on regression or categorical classification
- Spam detection
- Pattern detection
- Natural Language Processing
- Sentiment analysis
- Automatic image classification
- Automatic sequence processing (for example, music or speech)
Commons unsupervised applications include:
- Object segmentation (for example, users, products, movies, songs, and so on)
- Similarity detection
- Automatic labeling
Common deep learning applications include:
- Image classification
- Real-time visual tracking
- Autonomous car driving
- Logistic optimization
- Bioinformatics
- Speech recognition
http://citeseer.ist.psu.edu/index
- Introduction to Machine Learning, Third Edition By: E. Alpaydin
- A Course in Machine Learning (online) By: H. Daumé
- Pattern Recogniton and Machine Learning By: C. Bishop
- Machine Learning: a Probabilistic Perspective By: K. Murphy
- Coding the Matrix: Linear Algebra through Applications to Computer Science By: P. Klein
- Mastering matplotlib By: Mcgreggor D.
- Learning pandas - Python Data Discovery and Analysis Made Easy By: Heydt M.
- Learning Python By: Lutz M.
- Python 3 Text Processing with NLTK 3 Cookbook By: Perkins J.
- Artificial Intelligence: A Modern Approach By: Russel S., Norvig P.
- Deep Learning By: Goodfellow I., Bengio Y., Courville A.
- How to Create a Mind By: Kurzweil R.
- A Theory of the Learnable By: Valiant L.
- The Elements of Statistical Learning: Data Mining, Inference and, Prediction By: Hastie T., Tibshirani R., Friedman J.
- Mathematics: Its contents, Methods, and Meaning By: Aleksandrov A.D., Kolmogorov A.N, Lavrent'ev M.A.
- Statistics By: Freedman D., Pisani R., Purves R.
- An Introduction to Statistical Learning: With Application in R By: Gareth J., Witten D., Hastie T., Tibshirani R.
- Linear Algebra By: Poole D.
- Automatic Choice of Dimensionality for PCA By: Minka T.P
- Online Dictionary Learning for Sparse Coding By: Mairal J., Bach F., Ponce J., Sapiro G.
- The Optimality of Naive Bayes By: Zhang H.
- Probability, Random Variables and Stochastic Processes By: Papoulis A.
- Numerical Optimization By: Nocedal J., Wright S. J.
- Understanding variable importances in forests of randomized trees By: Louppe G., Wehenkel L., Sutera A., and Geurts P.
- Robust seed selection algorithm for k-means type algorithms By: Karteeka Pavan K., Allam Appa Rao, Dattatreya Rao A. V., and Sridhar G.R.
- Normalized Cuts and Image Segmentation By: Shi J., Malik J.
- A Tutorial on Spectral Clustering By: Von Luxburg U.
- Cluster stability: an overview By: Von Luxburg U.
- Finding Groups In Data: An Introduction To Cluster Analysis By: Kaufman L., Roussew P.J.
- Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems By: Sarwar B., Karypis G., Konstan J., Riedl J.
- Matrix Factorization Techniques For Recommender Systems By: Koren Y., Bell R., Volinsky C.
- Machine Learning with Spark By: Pentreath N.
- NLTK Essentials By: Hardeniya N.
- Unsupervised Learning by Probabilistic Latent Semantic Analysis By: Hofmann T.
- Latent Dirichlet Allocation By: Blei D., Ng A., Jordan M.
- A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text By: Hutto C.J., Gilbert E.
- Deep Learning By: Goodfellow I., Bengio Y., Courville A.
- TensorFlow for Machine Intelligence: A Hands-On Introduction to Learning Algorithms By: Abrahams S., Hafner D.
Free and Must Reads:
- http://mmds.org/#ver21
- http://gael-varoquaux.info/scikit-learn-tutorial/
- https://seat.massey.ac.nz/personal/s.r.marsland/MLBook.html
- https://www.kaggle.com/c/word2vec-nlp-tutorial#part-1-for-beginners-bag-of-words
- https://web.stanford.edu/class/cs124/lec/naivebayes.pdf
- http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
- http://adataanalyst.com/scikit-learn/countvectorizer-sklearn-example/
- https://towardsdatascience.com/natural-language-processing-on-multiple-columns-in-python-554043e05308