{"id":118,"date":"2017-09-27T10:39:35","date_gmt":"2017-09-27T10:39:35","guid":{"rendered":"http:\/\/www.nullplug.org\/ML-Blog\/?p=118"},"modified":"2017-12-18T13:18:13","modified_gmt":"2017-12-18T13:18:13","slug":"additional-sources","status":"publish","type":"post","link":"https:\/\/www.nullplug.org\/ML-Blog\/2017\/09\/27\/additional-sources\/","title":{"rendered":"Additional Sources"},"content":{"rendered":"

Textbooks<\/h2>\n
    \n
  1. Machine Learning: A probabilistic perspective<\/a>\u00a0by Kevin Murphy. The material in this book is closest to what we will cover in the course, but is unfortunately not available for free. Written by an academic and a practitioner of machine learning, this text is full of real world examples and applications, while eschewing the ad-hoc approach of other texts.<\/li>\n
  2. Introduction to statistical learning<\/a>\u00a0by\u00a0Gareth James<\/a><\/span>,\u00a0Daniela Witten<\/a><\/span>,\u00a0Trevor Hastie<\/a><\/span>\u00a0and\u00a0Robert Tibshirani.<\/a>\u00a0This textbook is available for free. It has minimal prerequisites and as a consequence does not touch on some topics of interest.\u00a0 On the other hand, it has detailed R exercises which are wonderful. <\/span><\/li>\n
  3. The elements of statistical learning<\/a>\u00a0by\u00a0Trevor Hastie,<\/a> Robert Tibshirani<\/a>, and Jerome Friedman. A more advanced version of the previous text (and also free), covering more material, and assuming more background in statistics.<\/span><\/li>\n
  4. Deep learning<\/a>\u00a0by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. A new text on this burgeoning field. Written by central figures in the area, this text surveys deep learning, includes many useful citations (a great deal of the literature is still in academic articles), provides good rules of thumb, and is a relatively light read. This book is very much from a practitioner’s perspective with less justification for methodology and less emphasis on mathematical rigor. My limited experience is that other papers in this area have a similar emphasis, which reflects the limited understanding of why these methods work very well in some instances and not so well in others.<\/li>\n
  5. Reinforcement Learning: An Introduction<\/a> by Richard Sutton<\/a> and Andrew Barto<\/a>. A free textbook that has been recently revised and covers quite a few topics.<\/li>\n<\/ol>\n

    Lecture series<\/h2>\n
      \n
    1. Trevor Hastie and Robert Tibshirani have made their\u00a0lectures and slides<\/a>\u00a0for their course on statistical learning available. These are very well done.<\/li>\n
    2. Machine Learning lectures from Georgia Tech<\/a>, these are very well done introductory lectures on machine learning directed toward engineers. I personally love the nerdy jokes interspersed throughout.<\/li>\n
    3. Deep learning course lectures<\/a>\u00a0by Nando de Freitas. Excellent lecture series on deep learning from Oxford for computer scientists. This lecture series is aimed at those with some more background.<\/li>\n
    4. Machine learning course lectures<\/a>\u00a0by Andrew Ng. A somewhat older lecture series from Stanford aimed at computer scientists.<\/li>\n
    5. Machine Learning 101<\/a> by Jason Mayes. A very well done overview of the subject.<\/li>\n<\/ol>\n

      Websites<\/h2>\n
        \n
      1. Datacamp<\/a>: An online learning site with an emphasis on data science. Includes mini-lectures and online programming exercises. A nice introduction on how to take the material we are learning and apply it. However, there is a noticeable bump in difficulty in proceeding to implement these methods from scratch on your own computer.<\/li>\n
      2. Andrej Karpathy’s blog<\/a>: A truly excellent sequence of blog posts on topics related to machine learning.<\/li>\n
      3. Kaggle<\/a>: Hosts machine learning competitions, allows machine learning practitioners to share information, and hosts many large datasets.<\/li>\n
      4. Seeing Theory<\/a>: A beautiful sequence of javascript visualizations for statistical concepts.<\/li>\n<\/ol>\n

        Programming references<\/h2>\n
          \n
        1. Introduction to R<\/a>.<\/li>\n
        2. Python 3 standard library.<\/a><\/li>\n
        3. Keras<\/a>\u00a0(a significantly simpler way to apply the Tensorflow (or another backend) library for building neural networks).<\/li>\n
        4. Tensorflow API<\/a>\u00a0(a rich library for building and using neural networks).<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

          Textbooks Machine Learning: A probabilistic perspective\u00a0by Kevin Murphy. The material in this book is closest to what we will cover in the course, but is unfortunately not available for free. Written by an academic and a practitioner of machine learning, this text is full of real world examples and applications, while eschewing the ad-hoc approach … Continue reading “Additional Sources”<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[2],"tags":[],"class_list":["post-118","post","type-post","status-publish","format-standard","hentry","category-supplementary-material"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p9dIpN-1U","jetpack_likes_enabled":true,"jetpack-related-posts":[{"id":33,"url":"https:\/\/www.nullplug.org\/ML-Blog\/2017\/09\/26\/machine-learning-overview\/","url_meta":{"origin":118,"position":0},"title":"Machine Learning Overview","author":"Justin Noel","date":"September 26, 2017","format":false,"excerpt":"Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it. Donald Knuth Introduction First Attempt at a Definition One says that an algorithm learns if its performance improves with\u2026","rel":"","context":"In "General"","block_context":{"text":"General","link":"https:\/\/www.nullplug.org\/ML-Blog\/category\/general\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/web.stanford.edu\/class\/cs234\/images\/header2.png?resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/web.stanford.edu\/class\/cs234\/images\/header2.png?resize=350%2C200 1x, https:\/\/i0.wp.com\/web.stanford.edu\/class\/cs234\/images\/header2.png?resize=525%2C300 1.5x, https:\/\/i0.wp.com\/web.stanford.edu\/class\/cs234\/images\/header2.png?resize=700%2C400 2x"},"classes":[]},{"id":35,"url":"https:\/\/www.nullplug.org\/ML-Blog\/2017\/09\/26\/supervised-learning\/","url_meta":{"origin":118,"position":1},"title":"Supervised Learning","author":"Justin Noel","date":"September 26, 2017","format":false,"excerpt":"A big computer, a complex algorithm, and a long time does not equal science. - Robert Gentleman Examples Before getting into what supervised learning precisely is, let's look at some examples of supervised learning tasks: Identifying breast cancer. A sample study. Image classification. List of last year's ILSVRC Winners Threat\u2026","rel":"","context":"In "Supervised Learning"","block_context":{"text":"Supervised Learning","link":"https:\/\/www.nullplug.org\/ML-Blog\/category\/supervised-learning\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":531,"url":"https:\/\/www.nullplug.org\/ML-Blog\/2017\/11\/17\/problem-set-5\/","url_meta":{"origin":118,"position":2},"title":"Problem Set 5","author":"Justin Noel","date":"November 17, 2017","format":false,"excerpt":"Problem Set 5 This is to be completed by November 23rd, 2017. Exercises Datacamp Complete the lesson: a. Machine Learning Toolbox R Lab: Write a function in R that will take in a vector of discrete variables and will produce the corresponding one hot encodings. Write a function in R\u2026","rel":"","context":"In "General"","block_context":{"text":"General","link":"https:\/\/www.nullplug.org\/ML-Blog\/category\/general\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":377,"url":"https:\/\/www.nullplug.org\/ML-Blog\/2017\/10\/11\/problem-set-1\/","url_meta":{"origin":118,"position":3},"title":"Problem Set 1","author":"Justin Noel","date":"October 11, 2017","format":false,"excerpt":"Problem Set 1 This is to be completed by November 27th, 2017. (THIS IS A TYPO: This should read October 26th, 2017). It is okay if you finish by this ridiculous first due date. Forewarning For several of these exercises, you will be asked to install software and\/or setup accounts\u2026","rel":"","context":"In "General"","block_context":{"text":"General","link":"https:\/\/www.nullplug.org\/ML-Blog\/category\/general\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":486,"url":"https:\/\/www.nullplug.org\/ML-Blog\/2017\/11\/03\/problem-set-3\/","url_meta":{"origin":118,"position":4},"title":"Problem Set 3","author":"Justin Noel","date":"November 3, 2017","format":false,"excerpt":"Problem Set 3 This is to be completed by November 9th, 2017. Exercises [Datacamp](https:\/\/www.datacamp.com\/home Complete the lesson \"Introduction to Machine Learning\". This should have also included \"Exploratory Data Analysis\". This has been added to the next week's assignment. MLE for the uniform distribution. (Source: Kaelbling\/Murphy) Consider a uniform distribution centered\u2026","rel":"","context":"In "General"","block_context":{"text":"General","link":"https:\/\/www.nullplug.org\/ML-Blog\/category\/general\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":538,"url":"https:\/\/www.nullplug.org\/ML-Blog\/2017\/11\/24\/problem-set-6\/","url_meta":{"origin":118,"position":5},"title":"Problem Set 6","author":"Justin Noel","date":"November 24, 2017","format":false,"excerpt":"Problem Set 6 This is to be completed by November 30th, 2017. Exercises Datacamp Complete the lesson: a. 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