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What did we learn?

Programming paradigms

Imperative programming: using statements to change a program's state.

                    nums = [1, 2, 4]
                    for i in range(0, len(nums)):
                        nums[i] = nums[i] ** 2

Functional programming: expressions, not statements; no side-effects; use of higher-order functions.

                   list(map(lambda x: x ** 2, [1, 2, 4]))

                    (map (lambda (n) (expt n 2)) '(1 2 4))

Programming paradigms #2

Object-oriented and data-centric programming.

                    innocent_bee = Bee(5)
                    horrible_ant = Ant(10)

                    (define t
                        (tree 3
                                (list (tree 1 nil)
                                    (tree 2 (list (tree 1 nil) (tree 1 nil))))))
                    (map label (branches t))

Declarative programming: State goals or properties of the solution rather than procedures.


                    calc_op: "(" OPERATOR calc_expr* ")"

Programming concepts

  • Data storage:
    • Primitive/simple types: booleans, numbers, strings
    • Compound types: lists, linked lists, trees
  • Environments: rules for how programs access and modify named objects
  • Higher-order functions: Functions as data values, functions on functions
  • Recursion: approaching a problem recursively, general recursive patterns
  • Mutability: mutable objects, mutation operations, dangers of mutation
  • Exceptions: Dealing with errors
  • Efficiency: Different programs have different time/space needs

Software engineering

  • Abstractions, separation of concerns
  • Specification of a program vs. its implementation
    • Syntactic spec (header) vs. semantic spec (docstring).
    • Example of multiple implementations for the same abstract behavior
  • Testing: for every program, there is a test.

Remember: code isn't just read by computers, it's also read by humans.

What's next?

Practice programming

  • Programming puzzles (HackerRank, LeetCode, Euler)
  • Programming contests (Advent of Code, Kaggle)
  • Hackathons
  • More paradigms and languages (Web dev, Embedded)
  • The open-source world: Go out and build something!
  • Personal projects
  • Above all: Have fun!

Future CS courses

  • CS61B: (conventional) data structures, statically typed production languages.
  • CS61C: computing architecture and hardware as programmers see it.
  • CS70: Discrete Math and Probablilty Theory.
  • CSC100: Data Science
  • CS170, CS171, CS172, CS174: β€œTheory”—analysis and construction of algorithms, cryptography, computability, complexity, combinatorics, use of probabilistic algorithms and analysis.
  • CS161: Security
  • CS162: Operating systems.
  • CS164: Implementation of programming languages
  • CS168: Introduction to the Internet
  • CS160, CS169: User interfaces, software engineering
  • CS176: Computational Biology

Future CS courses #2

  • CS182, CS188, CS189: Neural networks, Artificial intelligence, Machine Learning
  • CS184: Graphics
  • CS186: Databases
  • CS191: Quantum Computing
  • CS195: Social Implications of Computing
  • EECS 16A, 16B: Designing Information Systems and Devices
  • EECS 126: Probabilty and Random Processes
  • EECS149: Embedded Systems
  • EECS 151: Digital Design
  • CS194: Special topics. (E.g.) computational photography and image manipulation, cryptography, cyberwar.
  • Plus graduate courses on these subjects and more.
  • And please don't forget CS199 and research projects.

Plus EE courses...

  • EE105: Microelectronic Devices and Circuits.
  • EE106: Robotics
  • EE118, EE134: Optical Engineering, Photovotalaic Devices.
  • EE120: Signals and Systems.
  • EE123: Digital Signal Processing.
  • EE126: Probability and Random Processes.
  • EE130: Integrated Circuit Devices.
  • EE137A: Power Circuits.
  • EE140: Linear Integrated Circuits (analog circuits, amplifiers).
  • EE142: Integrated Circuits for Communication.
  • EE143: Microfabrication Technology.
  • EE147: Micromechanical Systems (MEMS).
  • EE192: Mechatronic Design.

Learn more Python!

Fun with Python πŸŽ‰ 🐍

What can you do with Python?

Almost anything! Thanks to libraries!

  • Webapp backends (Flask, Django)
  • Web scraping (BeautifulSoup)
  • Natural Language Processing (NLTK)
  • Data analysis (Numpy, Pandas, Matplotlib)
  • Machine Learning (FastAi, PyTorch, Keras)
  • Scientific computing (SciPy)
  • Games (Pygame)
  • Procedural generation - L Systems, Noise, Markov

Web scraping & Markov chains

Web scraping: Getting data from webpages by traversing the HTML.

Markov chain: A way to generate a sequence based on the probabalistic next token.

Markov diagram

πŸ‘‰πŸ½ Demo: Composing Gobbledygooks

Further learning: urllib2 module, BeautifulSoup docs, N-Gram modeling with Markov chains, CS70/EECS126 for Markov chains

Web APIs

API (Application Programming Interface): A way to access the functionality or data of another program.

Web APIs: A way to access the functionality or data of an online web service. Typically over HTTP or via JavaScript.

πŸ‘‰πŸ½ Demo: Movie Mashups

Further learning: urllib2 module, The Movie DB API, ProgrammableWeb

Turtle & L-systems

Turtle: A library for drawing graphics (as if a pen is controlled by a turtle).

L-system: A parallel rewriting system and a type of formal grammar, developed originally by a biologist to model the growth of plants.

Example: Axiom: A, Rules: A β†’ AB, B β†’ A

                    n = 0 : A
                    n = 1 : AB
                    n = 2 : ABA
                    n = 3 : ABAAB

πŸ‘‰πŸ½ Demo: L Trees!

Further learning: turtle module, Tutorial: Turtles and Strings and L-Systems, Algorithmic Botany: Graphical Modeling using L-systems, L-system examples

Natural Language Processing

NLP includes language modeling, spelling correction, text classification, sentiment analysis, information retrieval, relation extraction, recommendation systems, translation question answering, word vectors, and more.

πŸ‘‰πŸ½ Demo: Sentence trees!

Further learning: NLTK Book, NLTK Sentiment Analysis, Dan Jurafsky's lectures and books, Berkeley classes: INFO 159, CS 288

Demo: Supervised Machine Learning

Diagram of supervised ML process

πŸ‘‰πŸ½ Demo: Bee vs. Wasp?

Further learning: FastAI Documentation, Kaggle ML tutorial, Bias in ML, Berkeley classes: CS182, CS188, CS189

What do you want to do?

There are so many possible programs that haven't been made yet. What will you make?