# 10701: Intro to Machine Learning. Instructors: Pradeep Ravikumar, Manuela Veloso, Teaching Assistants:

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1 10701: Intro to Machine Instructors: Pradeep Ravikumar, Manuela Veloso, Teaching Assistants: Shaojie Bai Adarsh Prasad Otilia Stretcu Dimitris Konomis Satyapriya Krishna Sreena Nallamothu Lam Wing Chan Wenhao Qin George Stoica Lectures: GHC 4401, Mondays and Wednesdays, 10:30 11:50 AM Office Hours: Pradeep Ravikumar: GHC 8111, Mondays 1:00 2:00 PM Manuela Veloso: TBD Course Description: Machine learning is concerned with the study and development of automated systems that improve their performance through experience. Examples range from robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you. Objectives: This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning, and related disciplines and applications. Pre-requisites: Students entering the class are expected to have a pre-existing working knowledge of probability, linear algebra, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate. In addition, recitation sessions will be held to revise some basic concepts.

2 Outline of material: Foundations o Key Axes of ML: Data, Algorithms, Tasks o Data: Partially/Fully Observed, Interactive o Algorithms: Model-based, Model-Free o Tasks: Prediction, Description o Decision Theory, Generalization, Model Selection, Guarantees Regression o Linear, Polynomial Classification o Logistic Regression, Naïve Bayes, Support Vector Machines, Boosting, Surrogate Losses, Decision Trees Nonparametric Methods o K Nearest Neighbors, Kernel Regression and Density Estimation o Kernel Trick Unsupervised o Graphical Models o Clustering o Latent Variables Models, Expectation Maximization Sequence Models o Hidden Markov Models o State Space Models Representation o Random Features o Principal Component Analysis, Independent Component Analysis o Neural Networks, Deep Networks Reinforcement o Markov Decision Processes o Value Iteration, Q

3 Tentative Course Schedule: Date Instructor Topic Category Readings HW Out/Due Jan 17 MV Intro: Data, Algorithms, Tasks KM Chap. 1 Jan 22 PR Prob. Models: Estimators, Guarantees, MLE KM Chap. 2, 6 Foundations Jan 24 MV Prob. Models: Bayesian Estimation, MAP KM Chap. 5 Jan 29 PR Model-free Methods, Decision Theory HTF Chap. 2 HW 1 out Jan 31 MV Regression: Linear Regression CB Chap. 3 Feb 5 MV Regularized, Polynomial, Logistic Regression CB Chap. 3, 4 Feb 7 MV Classification: Naive Bayes, Generative vs Discriminative Prediction, CB Chap. 4 Feb 12 PR Classification: Support Vector Machines Parametric Methods KM Chap. 14 HW 1 due/ HW 2 out Feb 14 Guest Lect.Classification: Boosting, Surrogate Losses HTF Chap. 10 Feb 19 MV Decision Trees HTF Chap. 9 Feb 21 PR Foundations: Generalization, Model Selection HTF Chap. 7 Feb 26 MV Neural Networks and Deep CB Chap. 5, KM Chap. 28 HW 2 due/ HW 3 out Feb 28 PR Non-parametric Models: K nearest neighbors, kernel density estimation HTF Chap. 6, 13 Mar 5 PR Non-parametric Models: SVM, Lin Reg: primal + dual, Kernels, Kernel Trick Non-parametric Methods CB Chap. 6, 7 Mar 7 PR Non-parametric Models: Kernel Trick contd., possibly GPs CB Chap. 6, 7 HW 3 due (Mar 9) Mar 12 SPRING BREAK Mar 14 SPRING BREAK Mar 19 Guest Lect.Unsupervised : Clustering: Hierarchical, K Means HTF Chap Mar 21 Midterm Unsupervised : Clustering: Mixture of Gaussians, HW 4 out Mar 26 PR Expectation Maximization CB Chap. 9 Unsupervised Mar 28 PR Unsupervised : Latent Variable Models CB Chap. 9 Apr 2 PR Unsupervised : Graphical Models KM Chap. 10, 19, 20 Apr 4 PR Unsupervised : Graphical Models KM Chap. 10, 19, 20 Apr 9 MV Sequence Models: Hidden Markov Models Sequence KM Chap. 17 HW 4 due/ HW 5 out Apr 11 MV Sequence Models: State Space Models, other time series models Models KM Chap. 18 Representation : Feature Transformation, Random Representation Apr 16 TBD/PR Features, PCA HTF Chap Apr 18 TBD/MV Representation : PCA Contd, ICA HTF Chap Apr 23 MV RL: MDPs, Value Iteration, Q Reinforcement HW 5 due Apr 25 MV RL: Q learning in non-det domains, Deep RL Apr 30 PR Foundations: Statistical Guarantees for Empirical Risk Minimization May 2 PR and MVFinal Project Presentations Books: CB: Pattern Recognition and Machine, Christopher Bishop KM: Machine : A probabilistic perspective, Kevin Murphy HTF: The Elements of Statistical : Data Mining, Inference and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman Logistics: Class Website:

4 The class schedule, logistics, and lecture materials will be posted there. Discussion, Announcements: We will use Piazza for announcements, as well as the discussion board for the class. Textbooks: Lectures are intended to be self-contained. For supplementary readings, with each lecture, we will have pointers to either online reference materials, or chapters from the following books: Pattern Recognition and Machine, Christopher Bishop. Machine : A probabilistic perspective, Kevin Murphy. The Elements of Statistical : Data Mining, Inference and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman. Homeworks: There will be 5 homework assignments, approximately evenly spaced throughout the semester. The assignments will be posted on the course website, and on Piazza. We will use Gradescope for submitting, and grading assignments. You will get a late day quota of TBD days, which you can distribute among the five homeworks as you wish. Homeworks submitted after your late day quota will not be accepted. We expect you to use the late day quota for conference deadlines and events of the like, so we cannot provide an additional extension for such cases. In the case of an emergency (sudden sickness, family problems, etc.), we can give you a reasonable extension. But we emphasize that this is reserved for true emergencies. Collaboration Policy: The homeworks are structured to give you experience in both written mathematical exercises and programming exercises. While it is completely acceptable for you to collaborate with other students in order to solve the problems, we assume that you will be taking full responsibility in terms of writing up your own solutions and implementing your own code. You must indicate on each homework the students with whom you collaborated. Midterm: There will be one midterm, scheduled to be about halfway through the semester. The precise date is on the course website. The exam will consist of multiple choice and true/false questions, as well as short-answer questions. Class project: There will be a class project. You can form groups of up to TBD students. Further details on the project can be found on the website.

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