Instructor | Xiaohui Xie |
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Lectures | HH 262 MW 6:30-7:50pm |

Office Hours | DBH 4088 Wed 2-3pm |

Other Links | Piazza |

- Course description How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.
- Lecture schedule
- Course Introduction
- Model Complexity; Nearest Neighbors
- Bayes Classifiers; Naive Bayes; Bayes Error
- Linear Regression, Gradient Descent, Cross Validation, Regularization
- Linear Classification, Perceptron, Logistic Regression, Multi-Class, Cross-Entropy
- VC dimension, Structural risk minimization, AIC, BIC
- SVM, Lagrangian and Dual, Kernel Trick
- Decision Trees, Entropy, Information Gain
- Neural Nets, Back Propagation, Convolutional Neural Nets
- Ensemble of learners, Bagging, Boosting
- Clustering, K-Means, Agglomerative clustering, Gaussian Mixtures, EM
- Latent spaces models, PCA, SVD, Eigen-face, Recommendation Systems
- Reinforcement Learning, MDP, Value function, Policy iteration, Monte Carlo, TD, Q-learning
- Programming Assignments
- Due date
- Jan 17 (11:59pm), 2021
- Description
- Code/Data
- HW1-code.zip
- Submission
- Gradescope
- Due date
- Jan 29, 2021
- Description
- Code/Data
- HW2-code.zip
- Submission
- Gradescope
- Due date
- Feb. 12, 2021
- Description
- Code/Data
- HW3-code.zip
- Submission
- Gradescope
- Due date
- March 5, 2021
- Description
- Code/Data
- HW4-code.zip
- HW4-data.zip
- Submission
- Gradescope
- Due date
- March 15, 2021
- Description
- Code/Data
- HW5-code.zip
- Submission
- Gradescope
- Prerequisites Intro to AI, Calculas, Linear Algebra, Python Programming
- Grading Policy <
- Assignments: 30%
- Course Project: 15%
- Mid-term: 20%
- Final: 30%
- Participation: 5%
- Class participation, Polls, Discussions on Piazza, etc

- Homework Policy

Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike; now, websites like Kaggle host regular open competitions on many companies' data.

This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

- Machine learning by Kevin P. Murphy
- Deep Learning by Goodfellow, Bengio and Courville

Find our class page at: https://piazza.com/uci/winter2021/cs273p/home