Education

• Norms
• Singular value decomposition
• QR decomposition
• Linear least squares
• LU factorization
• Numerical stability
• Solving sparse linear systems
• Eigenvalues and eigenvectors
• High performing algorithms

• Mistake Bounded Learning
• PAC Learning
• Decision Trees
• Cross-Validation
• VC Dimension
• Perceptron
• Linear Regression
• Gradient Descent
• Boosting
• PCA and SVD
• Maximum Likelihood Estimation
• Bayesian Inference
• K-means and Expectation Maximization
• Multivariate and graphical models
• Neural networks
• Generative adversarial networks

• Discrete sample spaces
• Conditional probability
• Random variables
• Mathematical expectation
• Moment generating functions
• Joint distributions
• Correlation
• Simulation

Understand theory and practice of deep learning, drawing material from machine vision, machine translation, dynamical systems, audio processing, neural computing and human perception. Learn supporting mathematical concepts, including linear algebra, stochastic optimization, and hardware acceleration.

• Supervised and unsupervised learning
• Linear regression
• Logistic regression
• Support vector machines
• Decision trees
• Random forests
• Neural networks
• Clustering algorithms
• Dimensionality reduction techniques

Enhanced problem solving and algorithm design skills. Build on algorithms and problem-solving strategies learned in previous courses.

Use design, development, testing and refactoring techniques to build and evolve reliable, maintainable and scalable software systems. Covers a wide range of design patterns and principles. Also introduces software architecture and architectural patterns.

• Basic probability and random variables
• Estimation and its uncertainty
• Inference and interpretation for the linear model
• Prediction and model comparison
• Introduction to Bayesian statistics
• Binary data
• Evaluating classification models
• Data ethics and privacy

• Relational, deductive, and object-oriented data models
• Integrity constraints, query languages, and database design
• Various SQL and NoSQL database systems

Systems programming principles and concepts, including Linux systems programming, multiprocessing, concurrency, exceptional control flow, caching, sockets, protocols, and non-blocking I/O.

• Dynamic programming
• Greedy algorithms
• Graph algorithms
• Divide-and-conquer algorithms
• Linear programming
• Intelligent search algorithms

Societal impact of computer technology, the computer scientist's place in society, ethical issues. Reading, discussion, and writing seminar.

Practical linear algebraic computations and applications.

Concepts and applications of linear systems, matrices, vectors and vector spaces, linear transformations, determinants, inner product spaces, eigenvalues, and eigenvectors.

• Finite state automata, regular languages, and lexical analysis
• Push-down automata, context-free languages, and parsing
• Turing machines and unrestricted grammars
• Computability complexity and NP-completeness

• Introduction to grammars and parsing
• Predicate and propositional logic
• Proof techniques
• Sets, functions, relations, and relational data models
• Graphs and graph algorithms

• Create interactive web applications using HTML, CSS, and JavaScript
• Modularize, build, and package an application using a web framework
• Create a backend service using DNS, HTTPS, WebSocket, service endpoints, authentication, and data persistence
• Deploy applications and manage code

• Fundamental data structures and algorithms of computer science
• Basic algorithm analysis
• Recursion
• Sorting and searching
• Lists, stacks, queues, trees, hashing
• Object-oriented data abstraction

• Low level data representation and abstraction
• C programming and Assembly language
• Computer architecture and pipelining
• Memory hierarchy
• Dynamic memory allocation
• Linking

• Graphical displays and numerical summaries
• Data collection methods
• Probability
• Sampling distributions
• Confidence intervals and hypothesis testing involving one or two means and proportions
• Contingency tables
• Correlation and simple linear regression

• Techniques and applications of integration
• Sequences, series, convergence tests, and power series
• Parametric equations
• Polar coordinates